Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm

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Aiming at the challenges of multiple influencing factors, complex data characteristics, and limited data in streaming movie prediction, a feature fusion long- and short-term memory enhanced attention network (FFLSTMEA) was developed to achieve the short-term prediction of key indicators such as streaming movie revenue and to support business decisions. To address issues such as single data dimensions, difficulty in focusing on key information, limited data scale, and lack of diversity, several improvements were introduced. First, a feature fusion strategy was designed to integrate multi-dimensional features, including holiday factors, movie characteristics, principal component analysis (PCA) for time series dimensionality reduction, and platform exclusivity. These features were combined with a long- and short-term memory network to explore their internal correlations. Second, an attention mechanism was applied to dynamically assign importance to different time steps and features, enabling the model to focus on the most critical information based on time periods and movie types. Finally, the model’s capacity to capture data structures and variations was improved by using data augmentation techniques, such as flipping and scaling operations, to increase the dataset’s size and diversity. The experimental results show that the proposed algorithm FFLSTMEA achieves better prediction results with an average absolute error (MAE) of 3.50, a root mean square error (RMSE) of 5.28, and a coefficient of determination (R-squared) of 0.87 in the evaluation index. And compared with convolutional networks (CNN) class, long short-term memory (LSTM) class and Transformer class prediction methods, it performs better in terms of accuracy and stability, providing a more reliable basis for the operation and promotion of online movies.