# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 4 Results
### Analysis of Projected Results  
Figure 7 shows the comparison curves of different algorithms, including TrueValue, BiLSTM, CNN, CNN-Transformer, FFL, FFLA, FFLEA, GRU, LSTM, and Transformer. Among these algorithms, TrueValue serves as a benchmark, and the proximity of other algorithms' curves reflects their strengths and weaknesses; the BiLSTM curve fluctuates, and some intervals deviate from the real value; the CNN curve differs from the real value and the deviation increases in the latter part; the CNN-Transformer curve has a large deviation interval even though it tries to improve; the GRU curve has an insufficient response at peaks and troughs; and the LSTM curve has an insufficient response at peaks and troughs, as well as not being sufficiently large. The GRU curve is not responsive enough at the peaks and valleys; the LSTM curve has a lag or overfitting situation that causes the deviation to increase; and the Transformer has a significant deviation in certain  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature** & **Attention** & **Data En-** & **MAE** & **MSE** & **RMSE** & **R-Squared** & **MAPE** \\ \hline \(\times\) & \(\times\) & \(\times\) & 4.11 & 39.54 & 6.29 & 0.73 & 22.38 \\ \(\surd\) & \(\times\) & \(\times\) & 3.81 & 27.83 & 5.28 & 0.79 & 22.73 \\ \(\surd\) & \(\surd\) & \(\times\) & 3.58 & 24.41 & 4.94 & 0.81 & 22.19 \\ \(\surd\) & \(\surd\) & \(\surd\) & 3.50 & 27.92 & 5.28 & 0.87 & 24.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment line chart (“\(\times\)” means the module is not included and “\(\surd\)” means the module is included).  
intervals in a specific scene. Compared with the comparative methods, the proposed model has fewer prediction errors than the traditional LSTM and FFL in the initial stage (No. 0-10), which is attributed to the effective modeling of the early data dynamics by the multi-scale feature fusion module; secondly, in the complex fluctuation intervals (No. 10-20), the proposed model significantly outperforms the CNN and other models, and almost overlaps with the real value, which verifies the validity of the attention mechanism. Finally, in the long-tailed prediction interval (serial numbers 20-26), the proposed model can also be better approximated. It can be seen from the analysis that the FFLSTMEA model shows strong data comprehension and prediction ability in terms of accuracy, capturing key information precisely through unique feature extraction or data processing mechanisms, and performs well in different intervals in terms of stability; however, there are some problems in the proposed model that need to be improved, such as in the prediction of extreme events (e.g., step change in the box office data), the accuracy of the prediction results needs to be improved, and secondly, the number of parameters in the model needs to be improved. The second is that the number of model parameters increases compared to standard LSTM, which leads to time-consuming training. These results confirm that FFLSTMEA achieves the deep mining of movie box office temporal features by fusing the progressive optimization framework of feature selection (FFL) and attention modification (FFLA), etc., while keeping the computational efficiency controllable, and provides a new technological path for dynamic prediction in complex market environments.