Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

In the process of choosing the best scheme in the articial intelligence algorithm, it is impossible to accurately judge the nonlinear relationship between the innovation strategy and the lm and television postproduction scheme. An improved articial intelligence algorithm based on the integration of dynamic factors and the articial intelligence algorithm is proposed to reduce the disturbance ability of the articial intelligence algorithm and improve the analysis level of lm and television postproduction and innovation strategy. Firstly, the initial innovation strategy set of the production set is established by using dynamic factors, which makes it discrete and reduces the inuence of the scheme selection error on the results.­en, the production set is divided into dynamic subproduction sets by using the lm and television production theory, and each subproduction set seeks its own parallel innovation strategy. Finally, under the guidance of lm and television production theory, each subproduction set shares thematching of optimal solutions.­roughMATLAB simulation analysis and verication, the improved dynamic articial intelligence algorithm can improve the accuracy of judging the innovation strategy of lm and televisionworks in an uncertain environment and shorten the convergence time of global feature solution and is superior to the original selection method of lm and television production strategy. In addition, under that condition the initial weight scheme and the threshold scheme are set.­e articial intelligence algorithm is used to analyze the innovation strategy selection of youth idol works.­e results show that under dierent lm and television production requirements, the innovation strategy selection judgment of the articial intelligence algorithm is accurate and superior to the original lm and television production strategy selection method, which further veries the eectiveness of the articial intelligence algorithm proposed in this paper.


Introduction
With the rapid development of the social economy, the scale of the transmission network is expanding day by day, and it is developing in the direction of intelligence and complexity as stated by Zhou et al. [1]. erefore, it is particularly important to judge the choice of innovation strategy of lm and television works in a complex environment as illustrated by Zhang [2]. e key to judging the choice of innovation strategy of lm and television works is to judge the matching and the type of innovation strategy as stated by Sun et al. [3]. Film and television production theory can not only judge the nonlinear relationship between feature solution and innovation strategy selection as described by Sorkhabi et al. [4] but also solve the nonlinear and high-latitude innovation strategy selection problem, which is the main method to judge the innovation strategy selection of lm and television works at present as described by Singh and Kumar [5]. Literature research shows that the theory of lm and television production can classify the decomposed harmonic signals, shorten the diagnosis time, and improve the judgment e ect of harmonic signals as stated by Sikka et al. [6]. However, in the process of lm and television production theory classifying works, the complexity of lm and television works will a ect the judgment of model parameters and then a ect the nal judgment result of innovation strategy selection as stated by Sadok et al. [7]. Some scholars have put forward the arti cial intelligence algorithm. Although the arti cial intelligence algorithm can judge the choice of innovation strategy of lm and television works, there are relatively few postproduction parameters, and the overall production ability is greatly reduced under changeable and complex conditions as stated by Pazouki and Pourghorban [8]. erefore, some scholars have put forward a variety of innovative production strategies and improved the artificial intelligence algorithm, as shown in Table 1.
It can be seen from Table 1 that different types of film and television works have the same requirements in innovation and postproduction, so the artificial intelligence algorithm is needed as an aid; otherwise, the accurate selection of strategies cannot be realized. At the same time, some scholars have studied the theory of optimizing film and television production by the artificial intelligence algorithm ABC, which solves the problem of difficult optimization of postproduction parameters of film and television works and improves the reliability of innovative strategy selection diagnosis as stated by Park et al. [9]. rough the research and comparison of scholars at home and abroad, it is found that the artificial intelligence algorithm can quantitatively analyze film and television works, dig out the innovation points in the works, provide some reference for postproduction, guide producers to choose innovative strategies, and improve the production level of film and television works. In addition, artificial intelligence has the characteristics of fast analysis speed, simple operation process, and accurate analysis results, which is suitable for the analysis of a large number of film and television works. To sum up, the application of artificial intelligence in the field of film and television is relatively deep, but it is less applied in postproduction and innovative strategy selection. Compared with other productions, postproduction is the key to film and television works, so the demand for artificial intelligence algorithms is higher. e artificial intelligence algorithm can simplify the analysis process, improve the accuracy of calculation results, and integrate multiple indexes to analyze, and the final analysis results meet the actual requirements. According to the existing literature, the application of artificial intelligence in the production of film and television works will be deeper in the future and it will have a farreaching impact on innovation strategies. erefore, the integration of artificial intelligence and later works of film and television are the future development trend. From the analysis of the above literature, it can be seen that the influence of artificial intelligence on the later works of film and television is mainly the innovation of works, the rationality of strategies, the production effect of works, etc., so the corresponding optimization indicators also start from the above aspects. Based on this, this paper puts forward a dynamic artificial intelligence algorithm, optimizes the film and television production theory, judges the choice of innovative strategies for complex film and television works, and verifies the effectiveness of this aspect as stated by Owsley and Greenwood [10].

Film and Television Production eory.
Film and television production theory is a theoretical set of montage, lens combination, material editing, and other theories as stated by Naz [11]. Its solution set is the best combination of various film and television production theories, which has the characteristics of sparsity and integration and can be used for nonlinear analysis of film and television works. In the early 20th century, film and television production theories such as montage, material cutting, and sound insertion were widely used in postproduction, innovation strategy analysis, and other fields. In order to effectively combine and apply various learning models, especially the hinge loss function in the core function, the experience and technology in the discrete film and television works are combined, and the accuracy of the combined strategy results is improved through the kernel learning method [12].
Among them, w is the weight and λ is the threshold scheme. At that time | Y f | � lim δx⟶0 1, the number of film and television works was the least and the film and television works and classification intervals were the largest. When the weight ‖w‖ is the greatest, the innovation degree of the work is the highest, and it is 2/‖w‖ Mu, X. [13].
Among them, C is the deviation factor, which mainly reflects the deviation between film and television works and complexity.
, then the innovation strategy can be expressed as L(x i , y i , z i ). e results are shown in the following formula: To sum up, the postproduction parameter C sum in film and television production theory is the key to choosing innovative strategy of the artificial intelligence algorithm, and it is also the main postproduction parameter optimized in this paper as stated by Kushwaha et al. [14].

Artificial Intelligence Algorithm.
e artificial intelligence algorithm can optimize innovation strategy by simulating artificial behavior, including leading, assisting, and adjusting. During initialization, the number of film and television works and innovative schemes of works is the same, and the matching of different film and television works represents the optimal solution. Firstly, the initialization production set and film and television works are randomly generated, the film and television works are judged near the scheme with a better fitness scheme, and the "poor" scheme, about 1/2 of the number, is eliminated by comparison as stated by Hornung [15]. en, the auxiliary scheme uses the roulette strategy to judge the best scheme, gives corresponding weights, and makes greedy judgments around it to produce the 1/2 scheme. Finally, we give up the film and television works that do not meet the threshold scheme and judge the film and television works in other directions as shown by Ghasseminia et al. [16].
Assuming that the initial number of film and television works and innovative schemes is n and the random matching of film and television works is L � (x i , y i , z i ), x i , y i representing plane coordinates and z i is the difficulty, then the initial matching of film and television works is as follows: Among them, x i , y i , and z i are the maximum scheme with arbitrary matching and difficulty and x j max and y j min are the optimal schemes with various difficulties. z j min is a random number in the range of [0, 1].
We lead the scheme to randomly judge film and television works and make cross-judgment to update the matching of film and television works. Under the constraint of fitness, the maximum strategy is used to obtain the optimal matching of the corresponding weights, and the process of combining strategies is shown in the following formula: where o ∈ [0, n/2], i ∈ [n/2, n], φ ijz ∈ (−1, 1), and k ≠ i. e auxiliary scheme is to judge the best film and television works by probability p i and judge the neighborhood of the better film and television works to obtain the optimal matching. e combination strategy process is shown in the following formula: Among them, F(·) is a moderate function with different difficulties.
If the film and television works have not obtained the optimal solution after infinite cycle judgment, the film and television works judgment will be abandoned, the auxiliary scheme will be changed into an adjustment scheme, and new film and television works will be randomly generated according to formula (6) for the next judgment.
(1) We make a collection to judge the dynamic adjustment of the number of works analyzed in a single time as shown by Ghani et al. [17]. In the preliminary analysis, the adjustment scheme cannot guarantee the overall judgment and may choose a certain judgment "subjective scheme," which reduces the overall performance of the artificial intelligence algorithm. erefore, in the process of selecting the best scheme for film and television works, we should Mobile Information Systems try our best to expand the judgment range, judge the range near the optimal solution, and constantly adjust the number of works analyzed once. Some scholars make linear ρ adjustments to judge the number of works in a single analysis to reduce the randomness of the number of works in a single analysis but choose local "subjective schemes" near the optimal solution. In order to make up for the shortage of choosing the best scheme mentioned above, this paper introduces a dynamic adjustment factor ], and its combination strategy formula is shown in the following formula: Among them, Δ] i is an i times inertial scheme and F(x i , y i , z i ) is a moderate function of the i times update. e matching update of the lead scheme and the auxiliary scheme can be changed to the following formula: From formula (8), it can be seen that the scheme is K(Δd ik , Δq ik , Δb ik ) relatively small and large in preliminary analysis, which can expand the judgment range of the leading scheme, avoid selecting the local "subjective scheme," and keep the diversity of selecting the best scheme K(Δd ik , Δq ik , Δb ik ) as shown by Flament [18]. In the production stage of judgment, the scheme is relatively large and relatively small ], which narrows the judgment range, improves the neighborhood judgment ability of the leading scheme, and enhances the dynamic judgment performance of the arti cial intelligence algorithm as shown in Figure 1.
As can be seen from Figure 1, the dynamic adjustment of the number of works analyzed once in the production set can accurately determine the global polar scheme and the polar scheme represents the best combination of various lm and television production theories. erefore, the dynamic adjustment of the number of works analyzed in a single time can make the lm and television works meet the requirements and improve the accuracy of the combination strategy results as stated by Dai et al. [19]. At the same time, the data in Figure 1 show periodic changes. Although the whole data are smooth, there are still some breakpoints, which show that the arti cial intelligence algorithm segments the postproduction of lm and television works to achieve accurate selection of innovation strategies. In addition, the angle between the arti cial intelligence algorithm and the best strategy is consistent, which further illustrates the stability of arti cial intelligence.
(2) Introduction of factors has also been performed in this article.
When a certain lm and television work is collected many times and reaches the mining limit, the leading scheme will be changed into an adjustment scheme, looking for new lm and television works and combining new solutions of strategies. Because of its strong randomness and poor antidisturbance ability, the arti cial intelligence algorithm will fall into a local "subjective scheme" in the early stage of selecting the best scheme. e probability that the production set falls into the "subjective scheme" is positively correlated with the complexity of lm and television works. In order to make up for the above shortcomings, this paper introduces factors to reduce the complexity of lm and television works through the probability density function and helps the production set to jump out of the early local "subjective scheme". e combination strategy formula of the function is shown in the following formula: When lim δx⟶0 F(x i , y i , z i )/ F(x i , y i , z i ) is 1, it is the normal distribution of lm and television works Cauthy (0, 1). Cauthy, the random function of the work β tan[π · (α − 1/2)], α ∈ [0, 1]; Δx ik , Δy ik , and Δz ik are the change value of x ik , y ik , and z ik . At that time α 1, the representative lm and television works had the highest complexity, otherwise, the complexity was the lowest. Among them, Cauthy (0, 1) tends to the pole on both sides slowly, so the obtained scheme is better than Gauss (0, 1), which can e ectively reduce the subjective judgment rate. Moreover, the peak scheme of Cauthy (0, 1) is smaller than that of Gauss (0, 1), which can improve the in uence of subjective factors in function analysis. Based on the above analysis, the judgment formula of lm and television works can be expressed in the following formula:   Mobile Information Systems

Dynamic Innovation Strategy Analysis
(a) Optimization model of dynamic innovation strategy is as follows: Reasonable judgment of dynamic innovation strategy is the main index to measure the artificial intelligence algorithm. e dynamic optimization among the production set operations, such as leading, assisting, and adjusting, can not only balance the relationship between the global judgment and the local judgment of the production set but also improve the corresponding judgment ability Buendgens [20]. From formula (8), we can see that in the early judgment of film and television works, we pay a great deal of attention to the global production ability, and in the later judgment, we pay attention to the local judgment, so different production sets should dynamically judge the judgment strategy according to the conditions. At present, besides the dynamic artificial intelligence algorithm, there are other dynamic optimization models and adjustment strategy of individual schemes as shown in the following formula: (1) e overall scheme adjustment strategy results are shown in the following formula: (2) e results of the judgment strategy of film and television works are shown in the following formula: (3) e multiangle judgment strategy results are shown in the following formula: where t is the time to make set optimization.
In this paper, the artificial intelligence algorithm is improved in two aspects. On the one hand, every time the film and television works change, under the constraints of the weight of the mapping vector w and the threshold scheme of mapping λ, the set is made to randomly judge the strategy from the five models as stated by Buendgens [21] and the film and television works are collected many times. In the later stage of the judgment of film and television works, the judgment innovation strategy is gradually reduced, neighborhood judgment is carried out, and the diversity of production sets is maintained to improve the overall production ability. On the other hand, the global judgment and local judgment ability of the production set are balanced Δ] i , and the updated inertia scheme F(x i , y i , z i ), the moderation function, and the Lagrangian multiplier function are integrated to judge the best film and television works faster as stated by Budhwar et al. [21]. (b) Making dynamic reference strategy between sets.
Making the set reference is the main way to realize dynamic optimization. Based on the dynamic optimization of making the set, the artificial intelligence algorithm in this paper constructs a dynamic distributed reference strategy. Ben Cheikh [22]stated thatdifferent subproduction sets use different dynamic reference strategies, complex postproduction parameters, and operations. e production set is randomly divided into five subproduction sets, and each subproduction set represents a subinnovation strategy. In each iteration, the subproduction set will randomly judge different reference schemes. After each film and television work is collected, we compare the fitness schemes of different subproduction sets and the complexity of film and television works and record the global optimal matching. Other subproduction sets are aggregated to the optimal matching, which improves the judgment efficiency of the best film and television works.

Judgment Method Based on the Dynamic Factor and the Artificial Intelligence
Algorithm. e basic idea of the dynamic artificial intelligence algorithm is to use a variety of reference strategies to optimize the initial scheme and the threshold scheme of complex film and television works and obtain the global optimal scheme so that the judgment rate of innovative strategy selection of complex film and television works is the highest. e implementation steps of the artificial intelligence algorithm in this paper are shown in Figure 2: Step 1. We determine the structure and complexity of film and television works. According to the characteristics of practical problems, the nonlinear distribution structure of film and television works and the complexity of the artificial production set are determined c � 5.
Step 2. We initialize the film and television works. According to the related postproduction parameters, the Mobile Information Systems manual production set is initialized. Step 3. We determine the tness function. Using the theory of lm and television production, the arti cial production set is randomly generated and it is mapped to the changeable innovation strategy; the initial mapping vector weight and the mapping threshold scheme are obtained. According to the choice of innovative strategies for complex lm and television works, w 0.38 and λ 0.62. rough formulas (3)-(7), the arti cial set is improved, and the tness scheme of each lm and television work is combined.
Step 4. We combine global and local optimal matching of strategies. e initial production set is divided into ve subproduction sets, the tness is obtained, and the optimal global matching and the local optimal matching of each subproduction set are compared.
Step 5. We perform iteration of matching, adjusting, and updating lm and television works. According to the changes in lm and television works, the ve subproduction sets dynamically adjust factors, randomly judge strategies from ve optimization schemes, and incorporate the penalty factor C and the inertia weight Δ] i according to formulas (2) and (7). Step 6. We perform dynamic reference optimization of each subproduction set. After searching for a film and television work, the best global matching is selected, the matching information is shared with other subproduction sets to attract them to approach the best matching, and the best scheme is selected in the neighborhood.
Step 7. We judge whether the film and television works reach the maximum scheme m and whether the iteration times reach M. We repeat steps 1 to 5 if it has been reached; otherwise, we stop selecting the best scheme and return to the threshold scheme, weight, and global best matching.

Model Performance Analysis.
In order to further verify the performance of the dynamic artificial intelligence algorithm, four benchmark functions are selected, which are Rastrigin, sphere, Ackley, and Griewank. e test process is as follows : (a) e Rastrigin function results are shown in the following formula: where |x i | ∈ [0, 5.12] and i is 1, 2. e optimal scheme 0 is obtained at (0, 0). (b) e sphere function results are shown in the following formula: where |x i | ∈ [0, 5.12] and i is 1, 2. e optimal scheme 0 is obtained at (0, 0). (c) e Ackley function results are shown in the following formula: where |x i | ∈ [0, 10] and i is 1, 2. e optimal scheme 0 is obtained at (0, 0). (d) e Griewank function results are shown in the following formula: where |x i | ∈ [0, 100] and i is 1, 2. e optimal scheme of x i is 0 obtained at (0, 0). In this paper, the parameters of film and television works are set as follows: the total number of film and television works is 50, the number of iterations is 50, the optimization calculation is 20, the maximum inertia scheme Δ] max � 4, the optimal inertia scheme Δ] min � 0.3, and the complexity c � 0.32 are independent analysis of each group of works, and the result is an alternative strategy of 50 iterations. e results of the four test functions are shown in Table 2.
As can be seen from Table 2, the dynamic artificial intelligence algorithm is superior to the traditional analysis algorithm and its global optimal scheme and theoretical optimal scheme. Moreover, the selection range, substitution strategy, and combination strategy error of the dynamic artificial intelligence algorithm are smaller than those of the traditional analysis algorithm. e data in Table 2 show that the global optimization results of the artificial intelligence algorithm are better than those of traditional analysis methods, and the optimization schemes are higher than those of traditional analysis methods. Moreover, the data in the table are calculated by the scientific counting method, and the results are more accurate. In order to more intuitively reflect the performance of the best scheme selected by the test function in 4, the following convergence curves are given in Figures 3-6.
As can be seen from Figures 3-6, the dynamic artificial intelligence algorithm is faster and more stable in selecting the best scheme, which is superior to the artificial intelligence algorithm. erefore, the dynamic artificial intelligence algorithm performs better in the speed and accuracy of combination strategy, and the process of combination strategy is more stable.

Processing of Experimental Film and Television Works.
is paper selects the types of ethics, thriller, suspense, and police bandits as the research film and television works and collects 14 film and television works from January 20, 2020, to January 2022. After sorting preliminary film and television works, 102 film and television work innovation strategies and 12 postproduction schemes were obtained. According to the requirements of the state administration of radio, film, and television, the above film and television works are divided into four categories: emphasizing the early stage and neglecting the late stage, neglecting the early stage and emphasizing the late stage, whole process production analysis, and postproduction and innovation combination. e results are shown in Table 3. is paper judges the accuracy of theoretical prediction and actual results according to the way of theoretical judgment and actual detection. In order to avoid too many human factors in the collection of film and television works, the fill missing function is called mayhem as shown in Table 3. e first 1/2 of the total number of film and television works is used as training film and television works (62), and the last n/2 is used as the test set (40) for experimental comparison.

Experimental Results.
According to the experimental situation, it is determined that the structure of film and television work collection is 10-15-20, the scheme group Cauthy(0, 1) � 0.63, the maximum iteration times M � 50, and other postproduction parameters are the same. In this paper, the classification results of film and television works by the artificial intelligence algorithm are proposed, as shown in Figure 7.
rough comparative analysis, we can see that the classi cation of lm and television works based on the dynamic arti cial intelligence algorithm is discrete, which is closer to the actual postproduction distribution, while the classi cation of lm and television works based on the arti cial intelligence algorithm is relatively concentrated, which cannot meet the needs of actual classi cation. In addition, the distribution of lm and television works of the dynamic arti cial intelligence algorithm is not a ected by complexity, while the distribution of lm and television works of the arti cial intelligence algorithm is obviously a ected by complexity and becomes more concentrated with the increase in complexity. e reason is that the dynamic arti cial intelligence algorithm increases complexity processing coe cients c and factors Cauthy(0, 1) and establishes a mapping between the multiangle innovation strategy and single-angle innovation strategy. At the same time, the tness function is used to adjust the innovation strategy In order to further prove the e ectiveness of the model proposed in this paper, other comparative models are introduced for comparative analysis: (1) arti cial intelligence algorithm, (2) lm and television production theory combined with the arti cial intelligence algorithm, (3) lm and television production theory combined with the dynamic factor method, and (4) dynamic arti cial intelligence algorithm in this paper, and the results are shown in Figures 8  and 9.
As can be seen from Figures 8 and 9, the tness value of the dynamic arti cial intelligence algorithm is the highest and reaches the limit at the earliest. Under the same Note. ere is no signi cant correlation between the above data, * P > 0.05.
Number of post-production works (department) 8 7.5 7 6.5 6 5.   complexity, the stability of the dynamic arti cial intelligence algorithm is higher, followed by the arti cial intelligence algorithm, lm and television production theory combined with the dynamic factor method, and lm and television production theory combined with the arti cial intelligence algorithm. e reason is that the lm and television production theory reduces the in uence of complexity on the results of combination strategies, and dynamic factors     provide different optimization strategies to improve the accuracy of work recognition results, which is consistent with relevant research. From the aspect of complex postproduction types, the accuracy of different artificial intelligence algorithms is analyzed, and the results are shown in Table 4. As can be seen from the above table, the implementation rate of innovative strategy of the dynamic artificial intelligence algorithm is high, and the accuracy rate does not change with the change in the work type. e main reason is that the dynamic analysis of film and television works by factors makes its continuous combination strategy shorter and can change the types of works more flexibly. erefore, dynamic factors can not only reduce the influence of complexity on results but also quickly realize accurate analysis of different work types.

Conclusion
is paper presents an improved artificial intelligence algorithm based on film and television production theory as stated by Bemani [23] and combined with dynamic factors. By setting the threshold scheme, weight, and dynamic reference strategy, the identification optimization of postproduction is carried out.
e artificial intelligence algorithm model constructed in this paper can classify film and television works discretely as stated by Bagaric and Hunter [24], which makes the distribution of film and television works closer to the actual distribution of works. MATLAB simulation results show that the artificial intelligence algorithm constructed in this paper has high precision and speed of combination strategy, better convergence, adaptability to different complexity and work types, and high global production ability. In this paper, the discussion on the correlation between indicators and the implementation process of the artificial algorithm is insufficient and needs to be further deepened. ere are some limitations in the research of complexity dynamic switching, and future research will focus on the analysis of complexity switching, in order to improve the artificial intelligence algorithm proposed in this paper [25].

Data Availability
e experimental data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that they have no conflicts of interest regarding this work.