Empirical Model Research on Semantic Analysis Technology and Neural Network in Script Originality Evaluation

This paper proposes a script originality evaluation method based on semantic analysis and RNN model. First, the script text is deeply analyzed by semantic analysis technology to extract originality signature features such as plot novelty, character characteristics and language style. Then, RNN is used for automatic feature learning and originality detection. To improve the performance of the model, this paper adopts LSTM variants to handle long-term dependencies in the script and optimizes the model. The experimental results show that the RNN-based originality evaluation model has an accuracy rate and F 1 -score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 4 \%}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 7. 7 \%}$</tex> respectively, which is significantly better than the traditional rule-based evaluation method. At the same time, semantic analysis technology helps to extract the key originality features in the script, further improving the model's discrimination ability. By comparing with other models, this method has outstanding performance in the accuracy and applicability of originality detection, and has strong practicality and promotion value.