# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 5 Conclusions  
The FFLSTMEA model is put forth in this research to solve the issues of finiteness, data characteristics, and various influencing variables in the streaming movie prediction job. The FFLSTMEA model integrates multi-dimensional features through feature fusion and combines them with long- and short-term memory networks to explore the intrinsic associations of features; focuses on the key information with the help of the attention mechanism and assigns it dynamically; and expands the scale and diversity of the data by using data flipping and scaling. The experimental results show that the FFLSTMEA model delivers excellent performance in the evaluation indexes, such as having an MAE value of  
Figure 7: Comparison prediction curve chart.  
3.50, MSE value of 27.92, RMSE value of 5.28, R-squared value of 0.87, and MAPE value of 24.40, demonstrating better prediction accuracy and stability compared with other models such as CNN and LSTM, and it can provide a reliable basis for decision making in streaming movie operation and promotion. In the process of practical application, FFLSTMEA model can be deployed in the real-time decision-making system of streaming media platforms to realize rolling box office prediction by dynamically integrating multi-source features. In real-time scenarios, the system obtains user behavior data, social media heat index and platform scheduling information in real time via APIs to achieve box office prediction and drive personalized recommendation resource allocation, advertising space bidding adjustment and regional scheduling optimization.  
In the future, the FFLSTMEA model will integrate social media public opinion, real-time user review sentiment and cross-platform search trends to build a multimodal data-driven prediction framework to achieve the long-term accurate prediction of key indicators such as online movie share box office. On the other hand, with the continuous growth of data volume and the improvement of hardware computing power, more complex and efficient neural network architectures and optimization algorithms can be researched to further explore the deep information of the data, and enhance the model's adaptability and generalization ability, so as to better cope with the prediction challenges brought by the rapid changes in the online movie market, and help the online movie industry to achieve more scientific and reasonable resource allocation and business planning.  
Conceptualization, H.H. and T.T.; methodology, T.T.; software, T.T.; validation, H.H. and T.T.; formal analysis, H.H. and T.T.; investigation, C.L. and T.T.; resources, X.B. and T.T.; data curation, C.L. and T.T.; writing--original draft preparation, T.T.; writing--review and editing, C.L. and T.T. visualization, X.B. and T.T.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data sharing is not applicable. Data will be made available on request.  
The authors declare no conflicts of interest.