# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
###### Abstract  
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 (FFLTNEA) 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. streaming movies; prediction task; feature fusion; attention mechanism; data flipping and scaling +
Footnote †: journal: applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-000000-000000-000000-0000000-000000000000000000000000000000000000000000 prediction accuracy; [PERSON] et al. [4] used the superposition method to create an index system with consumer characteristics, movie product characteristics, and digital environment characteristics by integrating distributed random tree forest, extreme random tree, and generalized linear models. Compared with the characteristics of a digital environment, this model has a stronger generalization ability in box office prediction from the perspective of fusion, and the accuracy rate is as high as 99.1%. [PERSON] et al. [5] used multivariate linear regression and the back propagation (BP) neural network algorithm on IWOM quantities (such as the number of ratings, micro-drama reviews, and news) and discovered that although IWOM efficacy has a smaller impact, these quantities substantially enhance movie box office performance, and the predictive value is increased by 28.9% in comparison with the linear regression model. Similarly, [PERSON] et al. [6] found that the critical values of both the happiness index and movie box office varied linearly with the increase in the degrees of freedom and showed nonlinear characteristics through the optimized hierarchical long-short-term memory (HI-LSTM) model, which provided rich information for the prediction model; the use of the generalized Hurst function and Rennie's function enhanced the model's ability to recognize complex data structures; and the model achieved an accuracy of 92.4% in predicting the box office. [PERSON] et al. [7] used the Weighted K nearest neighbor (K-Nearest Neighbors, KNN) algorithm to study the market potential prediction of sci-fi movies.