# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 3 Fflstma
### Feature Fusion  
Feature fusion in streaming movie prediction improves the model performance by integrating multidimensional features based on the effective integration and synergistic utilization of different feature information.  
For holiday features, let the holiday feature vector be \(H=[h_{1},h_{2},\dots,h_{n}]\), where \(h_{i}\) denotes the feature value related to the ith holiday (e.g., type of holiday, association with weekend, etc.), which reflects the influence of the time periodicity law on the movie's box office, and its relationship with the box office can be determined by a specific time function or statistical analysis. The vector of factors influencing the movie's own characteristics is  
Figure 2: LSTM principle structure diagram.  
set as \(M=[m_{1},m_{2},\ldots,m_{n}]\), where \(m_{i}\) can be the quantitative value of the subject matter, cast, and other factors (e.g., obtained by scoring the popularity of the actors, the subject matter heat rating, etc.), which accurately depict the possible impact of the movie's inherent merit on viewers.  
In order to extract the principal components of the historical split box office data of the movie data and analyze them, the data are processed by a dimensionality reduction analysis using PCA, and the PCA time series dimensionality reduction scoring is set to P. Using the comprehensive eigenvalues obtained from the principal component analysis of the original time series data, the calculation process can be expressed as follows: set the matrix of the original movie data to be \(X\) (\(n\times p\) dimensions, \(n\) is the number of samples, and \(p\) is the number of eigecounts); firstly, compute the covariance matrix \(C=\frac{1}{n-1}X^{T}X\) of the movie data, and then solve to obtain the eigenvalues \(\lambda_{i}\) and eigenvectors \(v_{i}\) of these data; according to the size of the eigenvalues, the main eigenvectors are selected to represent the main features of the movie data; and finally, form the projection matrix \(W\), and then \(P=XW\).  
Whether the platform-exclusive broadcast feature is set as 1 or 0 (1 for exclusive broadcast, 0 for non-exclusive broadcast), this feature directly affects the distribution channel and exposure of the movie. When combining these features with LSTM, in the model, LSTM learns the sequence of historical split box office data, and L (the feature representation associated with split box office acquired during a sequence of time-step hidden state processing) is set as the LSTM's output.  
In the process of feature fusion, each feature is first linearly transformed to fit the fusion dimension; e.g., the holiday feature \(H\) is transformed to \(f_{H}(H)\) by the linear layer \(f_{H}\), the movie's own characteristic influence factor \(M\) is transformed to \(f_{M}(M)\) by \(f_{M}\), the PCA time series downscaling score \(P\) is transformed to \(f_{P}(P)\) by \(f_{P}\), and the whether-platform-only feature \(E\) is transformed to \(f_{E}(E)\).  
Then, feature fusion is performed and the fused feature vector is as follows:  
\[F=\left[\begin{array}{c}L\\ f_{H}(H)\\ f_{M}(M)\\ f_{P}(P)\\ f_{E}(E)\end{array}\right] \tag{3}\]  
Finally, the completely connected layer \(f_{final}\) maps the fused features to the expected output, so that the predicted value \(y=f_{final}(F)\).  
Through such a feature fusion approach, the model is able to deeply excavate the intrinsic correlation relationship between different features, so as to improve the prediction accuracy and generalization ability, in order to better cope with the task of predicting key indicators such as streaming movie split box office, and to provide powerful support for relevant decision making.