# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 2 Related Work
(2019) used a hierarchical attention mechanism (HAM), which may serve as the foundation for a movie recommendation system, to forecast a movie's performance from the viewpoint of the viewer. An HAM, which can capture the importance of movie-related features from different levels, and the Transformer architecture, which helps to handle long sequential data, etc., were combined to build a model to achieve an automatic and reasonable rating for movies, which assists the user to better understand the quality level of the movie. [PERSON] et al. (2019) adopted a multimodal fusion approach to integrate data from different modalities (such as video, audio, and other movie-related data modalities), and at the same time, applied the self-attention mechanism in the fusion process to highlight the key information related to the audience's emotion in each modality, and predict the emotional state of moviegoers. [PERSON] et al. (2019) employed recurrent neural networks to conduct the deep feature mining of movie trailers, and due to the fact that recurrent neural networks are good at dealing with sequential data, they are able to capture the time-series features in these kind of video data, which can help users better understand the quality level of the trailers. Because recurrent neural networks are good at processing sequence data, they can effectively capture the time series features in video data such as trailers, and to successfully forecast the movie's ultimate box office income, build the related box office revenue prediction model using the in-depth characteristics that were mined. Furthermore, throughout the past two years, a few sophisticated models have been put forth: By eliminating the trend component and utilizing the attention mechanism, TDformer increases the precision and effectiveness of time series forecasting ([PERSON] et al., 2019); By employing a multi-scale pyramid structure to capture the intricacy of the data, SageFormer enhances multi-variate time series forecasting ([PERSON] et al., 2019). SageFormer emphasizes temporal correlation, which is particularly suited for the task of long-term multivariate forecasting ([PERSON] et al., 2019); Crosformer provides more accurate forecasts by using cross-dimensional dependencies to capture the intrinsic relationship of the data ([PERSON] et al., 2019). Time prediction models have evolved from traditional statistical methods to deep learning techniques, and deep learning RNNs and their variants have been applied in multiple fields and explored in small-sample processing. In movie-related research, neural networks are employed in personalized recommendation, multimodal analysis, box office prediction, etc. However, there are problems such as insufficient feature integration, inaccurate focusing of information, and data processing limitations in the task of streaming movie prediction. In this paper, the FFLSTMEA model is enriched by multi-dimensionalfeature fusion and an attention mechanism to accurately focus the key information, and data flipping and scaling are employed to expand the rich data, compared with CNN, LSTM, BiLSTM, GRU, Transformer, CNN-Transformer, TDformer, MTPNet, SageFormer, Crosformer, and other models in the short-term prediction of the key indicators of streaming movies in terms of accuracy and stability, thus providing a more reliable basis for the decision making in the operation and promotion of streaming movies to compensate for the shortcomings despite the existing advantages.