## 5 Experimental Results
### Comparative Analysis of Models  
To verify the excellent performance of the improved Elman neural network algorithm proposed in this paper, the convergence speed and accuracy of the proposed model and the original Elman [23] and RBF (Radial Basis Function) neural networks [24] are compared. The convergence of the improved Elman neural network and the original Elman neural network is shown in Figures 4 and 5. It can be seen from Figures 4 and 5 that the original Elman neural network has iterated more than 360 times, while the improved Elman neural network has only been iterated more than 160 times under the same experimental environment. The proposed model takes significantly less time on enterprise collaboration performance evaluation data, which demonstrates the effectiveness of the improved learning algorithm. The enterprise collaboration performance evaluation data used in the experiment are representative and universal, so the experimental conclusions are also applicable to other enterprise collaboration performance evaluations of this type. \begin{table}
\begin{tabular}{c c c} \hline \hline
**The Numbers of Neurons in** & **Error (MSE)** & **Training Times (Epochs)** \\ \hline
6 & 0.0009996023 & 180 \\
7 & 0.0009992489 & 179 \\
8 & 0.0009993312 & 170 \\
9 & 0.0009992097 & 167 \\
10 & 0.0009991308 & 160 \\
11 & 0.0009990843 & 159 \\
12 & 0.0009994574 & 154 \\
13 & 0.0009997011 & 152 \\
14 & 0.0009994827 & 151 \\
15 & 0.0009998314 & 148 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of network performance with different numbers of neurons in the hidden layer. Figure 3: Model performance comparison for different numbers of neurons in the hidden layer. To verify the performance of the proposed model, 5-fold cross-validation and relative error are used to describe the performance of the model. The dataset is randomly divided into five disjoint subsets, one of the subsets is selected as the test set in turn, and the rest are used as the training set. Finally, the results of the five tests are averaged. The results of the cross-validation experiments are shown in Table 5. The formula for calculating the relative error RL is as follows:  
\[RL=\frac{\sum_{k=1}^{n}\frac{\left|y_{k}^{{}^{\prime}}-y_{k}\right|}{y_{k}^{{}^ {\prime}}}}{n} \tag{28}\]  
where \(n\) is the number of samples in the test set, \(y_{k}^{{}^{\prime}}\) is the target output value, and \(y_{k}\) is the actual output value of the model. From Table 5, it can be seen that the relative error of the proposed model is lower than the other two models in all five experiments. Figure 4: Convergence process of improved Elman neural network. Figure 5: Convergence process of Elman neural network. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & **Elman Neural Network** & **RBF Neural Network** & **Improved Elman Neural Network** \\ \cline{2-4}  & **Relative Error** & **Relative Error** & **Relative Error** \\ \hline
1 & 6.81\% & 7.52\% & 4.56\% \\
2 & 6.16\% & 7.23\% & 4.42\% \\
3 & 6.49\% & 7.92\% & 4.77\% \\
4 & 6.99\% & 7.80\% & 4.96\% \\
5 & 6.74\% & 7.68\% & 4.45\% \\ \hline Average & 6.64\% & 7.63\% & 4.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-validation experimental results of the proposed model with other models. Subsequently, the mean error of the proposed model over five experiments is compared with the original Elman and RBF neural networks. The comparison results are shown in Figure 6 and Table 6.  
It can be seen from Figure 6 that the error of the model proposed in this paper is higher than other models for only a few samples, and the error on most of the remaining samples is much lower than that of other models, thus showing a better overall evaluation accuracy. It can be seen from Table 6 that the relative error of the proposed model is only 4.26%, while the relative errors of RBF and the original Elman neural network are 7.28% and 6.15%, respectively. Compared with the other two methods, the relative error of the proposed model is reduced by an average of 36.14%.