## 4 Results and Discussion
### Weight Analysis of the Evaluation Indexes Using the ANP Method  
To comprehensively evaluate the application maturity of enterprise AI technology, it is necessary to evaluate the impact of each index on maturity. By using the questionnaire method to collect data and constructing a pairwise-comparison judgment matrix, the quantitative relationships among the indexes in the evaluation index system can be clarified, the weight of each evaluation index can be calculated, and the foundation for a comprehensive evaluation based on the Fuzzy-ANP method can be laid. The ANP model was constructed based on the structure of the nine-scale method, and two importance scale factors were compared to construct a questionnaire to obtain expert opinions, and a judgment matrix of the original data was constructed. The eigenvalue method was used to test the consistency of the constructed judgment matrix. According to the consistency test rule, if the judgment matrix has a determination coefficient CR \(<\) 0.1, it indicates that after the consistency test, the index weight is within the acceptable range. After testing, the consistency test results of all the judgment matrices constructed in this study were less than 0.1, and all passed the consistency test. SD software was used to perform the calculations. The construction of a supermatrix is a process in which the relative importance of the elements in the network layer is obtained via pairwise comparisons. Based on the judgment matrix, a weighted matrix was constructed, the normalized eigenvectors of each judgment matrix were summarized to obtain the block of a supermatrix, and then the supermatrix was obtained. The value in the supermatrix represents the importance of the column elements to the row elements, and corresponds to the local weight of each element in the element group. In accordance with the ANP principle, column normalization of the supermatrix was carried out, and the weighted supermatrix was obtained by processing the supermatrix and the weighted matrix. To obtain stable element weights, the stability of the weighted supermatrix was processed, and the limit supermatrix was further calculated via the limit matrix calculation method. Finally, the limit supermatrix was obtained, and the global weight of the index system was obtained by solving it. The final index weight table shown in Table 10 reflects the experts' judgment of the importance of the enterprise AI technology application maturity indexes, which can be used to guide subsequent maturity evaluation research and provide a decision-making reference for enterprises to promote the application of AI technology. The weights of the first-level indexes ranked from highest to lowest were \"intelligent business operations\", \"intelligence strategy\", \"intelligent organizational management\", and \"intelligence technology\". This result provides some enlightenment. Judging from the weights of the second- and third-level indexes, film and television enterprises should first attach great importance to intelligent strategies in the application process of AI technology. The most influential factor in technology was the research and development of intelligence technology and equipment. The most influential factor in business operations was smart shooting. The most influential factor in organizational management was corporate culture. From the perspective of first-level indexes, it is especially necessary to promote the intelligent business operations from multiple perspectives, such as film and television project planning and development, project production, and project marketing. In addition, we should pay attention to the intelligent organizational management models and constantly improve the application level of AI technology.