# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 4 Results
### Comparative Experimental Analysis  
To verify the effectiveness of the proposed algorithm, all experiments in this paper are conducted under the same hardware and software environments: the hardware environment is an Intel (R) Core i7-10700K CPU @3.80 GHz, with 32.0 GB RAM and an NVIDIA GeForce RTX 2060 SUPER graphics card. The software environment is Python3.7 built using Pycharm and the Pytorch1.13 deep learning environment for experimentation. Table 2 shows the results of the comparison experiments of different models (CNN, LSTM, BiLSTM, GRU, Transformer, CNN-Transformer, FFLSTMEA) in terms of a number of metrics (MAE, MSE, RMSE, R-squared, MAPE). MAE: The lower the value, the better the performance, among which the MAE value of 3.50 of FFLSTMEA is the best (red), followed by BiLSTM's 3.59 (blue), and the third best is MTPNet's 3.61 (light green). MSE: The lower the value, the better the performance. MAE: The lower the value, the better the performance, among which FFLSTMEA's MAE is 3.50 (red), followed by BiLSTM's 3.59 (blue), and the third best is MTPNet's 3.61 (light green). MSE: the lower the value, the better the performance, among which SageFormer's MSE is 26.12 (red), followed by GRU's 27.15 (blue), and the third best is BiLSTM's 27.88 (light green). RMSE: the lower the value, the better the performance, with GRU's RMSE value of 5.21 being the best (red), followed by BiLSTM's and FFLSTMEA's 5.28 (blue), and MTPNet's 5.33 (light green). R-squared: the higher the value, the better the model fit, with FFLSTMEA's 0.87 being the best (red), followed by FFLSTMEA's 0.85 (blue), and FFLSTMEA's 0.86 (light green). R-squared: the higher the value, the better the model fit, with 0.87 for FFLSTMEA (red), followed by 0.81 for MTPNet (blue), and 0.79 for Crossformer (light green). MAPE: the lower the value, the better the model fit, with 19.82 for BiLSTM (red), followed by 22.38 for LSTM (blue), and 23.11 for Crossformer (light green). SRCC: The larger the value, the better the model fit, 0.89 (red) for FFLSTMEA, 0.83 (blue) for MTPNet, and 0.81 (light green) for Crossformer. By comparing the quantitative indexes, the model proposed in this paper achieves better prediction results. The proposed FFLSTMEA model performs well in several key indicators, e.g., the evaluation index MAE value is 3.50 and R-squared value is 0.87, which represents an obvious advantage compared with other models; this is because the MAE and R-squared indexes are used to measure the data fitting effect, because the proposed model FFLSTMEA realizes multi-featured data fusion during data processing and prediction, so compared with other common models such as CNN, LSTM, and Transformer, it achieves better prediction results, and uses the attention mechanism in the prediction process to focus on the key information of the prediction results. Because the proposed model FFLSTMEA realizes \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models\textbackslash\{}Metrics** & **MAE** & **MSE** & **RMSE** & **R-Squared** & **MAPE** & **SRCC** \\ \hline CNN & 4.71 & 43.90 & 6.63 & 0.68 & 28.60 & 0.70 \\ LSTM & 4.11 & 39.54 & 6.29 & 0.73 & 22.38 & 0.74 \\ BiLSTM & 3.59 & 27.88 & 5.28 & 0.78 & 19.82 & 0.77 \\ GRU & 3.72 & 27.15 & 5.21 & 0.78 & 23.35 & 0.78 \\ Transformer & 4.60 & 53.00 & 7.28 & 0.65 & 23.74 & 0.68 \\ CNN- & 4.46 & 45.29 & 6.73 & 0.69 & 23.62 & 0.72 \\ Transformer & 4.55 & 38.29 & 5.98 & 0.72 & 25.32 & 0.74 \\ TDoformer & 3.61 & 29.27 & 5.33 & 0.81 & 24.10 & 0.83 \\ MTPNet & 3.97 & 26.12 & 5.70 & 0.72 & 23.29 & 0.75 \\ Crossformer & 4.67 & 33.94 & 6.13 & 0.79 & 23.11 & 0.81 \\ FFLSTMEA & 3.50 & 27.92 & 5.28 & 0.87 & 24.40 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experiment.