## 1 Introduction  
With the development of information technology and economic globalization, market competition has become increasingly fierce. Market demands are becoming increasingly diversified and individualized, and the expectations for products of customers are also becoming higher [1]. How to respond quickly to the market has become the key to competition among enterprises. To cope with these new challenges, more and more enterprises are cooperating in their core business links to quickly respond to market demands and enhance their competitiveness [2]. A division of labor based on specialized product parts forms a manufacturing enterprise collaboration network (MECN). The competition among enterprises has been transformed from a competition over the product quality and production cost of a single enterprise too competition over the overall collaboration performance of the enterprises and their collaborators. The essence of MECNs is to provide services such as collaborative product design, collaborative manufacturing, supply chain management, customer relationship management and information sharing among enterprises, and ultimately to realize various collaborative activities across enterprise boundaries. In recent years, much meaningful research has been carried out on the construction of MECNs. [PERSON] [3] investigated the clustering characteristics of an enterprise collaboration network through an integrating analytic hierarchy process and complex network theory. [PERSON] [4] proposed a probability-based gray comprehensive evaluation method to simulate the preference and uncertainties for partner selection problems in MECNs. [PERSON] [5] investigated an empirical MECN from the viewpoint of complex network theory to explore the topology and evolutionary process of MECNs, and to construct an evolutionary model to reproduce the topological properties found in the empirical network. [PERSON] [6] studied the MECN modeling and feature analysis of complex products, and proposed an enterprise partner selection model based on entropy weight and TOPSIS. [PERSON] [7] systematically reviewed the research status of collaborative networks and proposed a comprehensive multi-level framework for describing the logic and management of collaborative networks. [PERSON] [8] studied collaborative processes in hierarchical manufacturing networks and proposed potential future research lines to fully support collaborative processes in non-hierarchical manufacturing network environments. The current research on MECN mainly focuses on its formation process and characteristic analysis. There are relatively few studies on the collaborative relationship between manufacturing enterprises. The collaborative relationship of enterprises will directly affect both parties, and may even affect other related enterprises, resulting in a cascading effect that affects the operation of the entire alliance. Furthermore, the collaborative relationship between all enterprises in the alliance will determine the quality and cost of the product, thereby affecting the competitiveness of the entire alliance. Therefore, it is of great importance to study the evaluation of the collaboration performance between enterprises. The most common methods are the mathematical programming approach, uncertainty theory, swarm intelligence algorithm, BP neural network, and so on. However, some existing methods also have some shortcomings. Sometimes, subjective influence is too serious when deforming the weight of the index. Moreover, the evaluation data of enterprises in different periods usually show a certain correlation, and the output of BP neural networks is only determined by the current input when dealing with the evaluation problem, which deviates from the reality. To handle these problems, this paper conducts special research on the collaboration performance between enterprises in MECN.