## 6 Conclusions  
This paper proposes an enterprise collaboration performance evaluation method based on an improved Elman neural network and AHP-EW. The enterprise collaboration performance evaluation index system is established from the three dimensions of enterprise  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Collaborative Relationship**} & \multirow{2}{*}{**Expected**} & \multicolumn{2}{c}{**Elman Neural Network**[23]} & \multicolumn{2}{c}{**RBF Neural Network**[24]} & \multicolumn{2}{c}{**Improved Elman Neural Network**} \\ \cline{3-8}  & & **Predicted** & **Relative** & **Predicted** & **Relative** & **Predicted** & **Relative** \\  & & **Value** & **Error** & **Value** & **Error** & **Value** & **Error** \\ \hline \(E_{1}\) to \(E_{6}\) & 75 & 69.613 & 7.18\% & 80.302 & 7.07\% & 78.273 & 4.36\% \\ \(E_{7}\) to \(  
finance, enterprise business and enterprise strategy, and the weights obtained by the AHP and EW methods are synthesized by the minimum deviation method, which takes into account the influence of expert experience and data fluctuations simultaneously. Then, an improved Elman neural network model is proposed, in which the additional momentum and adaptive learning rate methods are used to improve the network's learning algorithm. The Elman neural network was used to convert human subjective judgments into objective statistical data to improve the rationality, intuition and credibility of the evaluation results. Finally, the proposed model is verified by taking the data of a manufacturing enterprise and its partners as an example. The optimal network structure for enterprise collaboration performance evaluation is determined through repeated experiments. The comparison results with original Elman and RBF neural networks show that the convergence speed of the proposed model is improved by about 50%, and the relative error is reduced by about 30% on average, which can provide a valuable reference for enterprise collaboration and management.  
Due to the limitation of external objective conditions, both qualitative and quantitative indicators are used when constructing the evaluation index system for enterprises' collaboration performance. Although the fuzzy comprehensive evaluation method is used to deal with qualitative indicators, qualitative indicators occupy a large proportion. In this process, qualitative indicators may be affected by the subjective consciousness and quality of the experts. Due to the fact that the number of experts in the expert database is relatively small, the number of experts is limited, and the breadth of prior knowledge accumulation is limited, which needs to be continuously enriched. The number of enterprise data samples should also gradually expand, which is more conducive to model learning and more accurate evaluation accuracy.  
Conceptualization, Y.J.; methodology, J.Z.; validation, X.D.; formal analysis, D.H.; writing-original draft preparation, J.Z.; writing--review and editing, J.Z.; supervision, D.H., B.G. and Y.J. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Key R&D Program of China [grant number 2020 YFB1707900, 2020 YFB171800].  
Not applicable.  
The authors declare no conflict of interest.