# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
###### Abstract
The results showed that the number of audience votes, the movie's running time, and the Internet Movie Database (IMDB) ratings all significantly positively correlated with box office performance, and the model's accuracy reached 93%. The results of the experiment demonstrate that even though previous research has produced certain results, the following shortcomings persist: (1) in terms of feature extraction, the combined effects of holidays, platform exclusivity, and other factors relating to movie characteristics and time series have not been fully considered, resulting in an imperfect feature system; (2) in terms of data processing, some models do not use effective data expansion strategies, so their means of generalization ability is weak, and it is difficult to cope with the complex market environment; (3) in terms of information focusing, most models are unable to filter key information dynamically and are easily disrupted by irrelevant information, which reduces their prediction accuracy. In summary, the traditional movie prediction model suffers from the insufficient consideration of features and is susceptible to the interference of irrelevant information, which leads to low prediction accuracy. A movie prediction algorithm based on multidimensional feature fusion with enhanced attention network is proposed. First, the internal correlation of movie data is mined using bi-directional long-term and short-term memory networks to improve the extraction of movie features, and then the attention mechanism module is used to mine the key information of different periods and genres of movies, giving different degrees of importance. Finally, the feature information of the movie is evaluated from four perspectives, and these features are combined to output the prediction results of the streaming movie breakdown box office and other key indicators, which solves the problem of the insufficient coupling of complex features in traditional methods and improves the prediction results of the model. The model outperforms other popular models in many evaluation metrics, which can provide a solid foundation for decision making in streaming movie operation, promotion and other matters. The main contributions of this paper are summarized as follows:  
1. Design a feature fusion mechanism to innovatively integrate the film's own influence factor, time series dynamic factor, platform operation, and other features to solve the limitation of single-dimension features in movie prediction, and deeply combine with long-short-term memory (LSTM) to construct the time series feature interaction space, so as to build a comprehensive database for online movie heat rating prediction, and make the prediction comprehensive and comprehensive, with information from many aspects. This can effectively solve the problem of the insufficient coupling of complex features in traditional methods. 2. Aiming at the time sensitivity and genre variability of movies, the attention mechanism is designed to dynamically adjust the weight allocation of LSTM hidden state and static features, dynamically assign different importance to LSTM output and each feature, capture key information, eliminate irrelevant interference, and strongly improve the prediction accuracy. 3. Data flipping and scaling operations are used to expand data scale and enrich diversity, enhance the model's ability to understand and grasp the changes in data structure and magnitude, and strengthen the generalization performance, so as to successfully solve the overfitting problem caused by data sparsity in the field of online movies, in order to adapt to the complexity and variability of the online movie market. The rest of this paper is structured as follows. Section 2 states the relevant work of time prediction models. Section 3 provides a detailed description of the proposed FFLSTMEA model, as well as the multi-dimensional feature fusion and enhanced attention network movie prediction algorithm. Section 4 presents and analyzes the experimental results.