Research on service clustering for manufacturing network by deep learning is an effective method for service discovery and service management in manufacturing industries. Manufacturing service clustering can be used to solve the problem of service selection and composition in cloud manufacturing. After clustering characteristics of graph data, this paper analyzes and studies the manufacturing network structure, and finds the network characteristics formed by clustering. A deep manufacturing cloud service clustering model using pseudo-labels (DSCPL) is proposed, which combines graph topology and node features to cluster nodes with similar attributes. The specific contributions of the paper are as follows: 1) Defining a parametric nonlinear map embedded the graph space containing graph structure (adjacency information) and node information (features) into a low-dimensional feature space. 2)Building an auxiliary target distribution to realize the self-learning mechanism in order to adapt to the clustering task, and 3) Realizing the unsupervised clustering method. Comprehensive experiments show that the clustering effect of this method is better than the current advanced deep clustering methods on public datasets and simulated datasets.

基于深度学习的制造网络服务聚类研究是制造业服务发现和服务管理的有效方法。制造服务集群可以用来解决云制造中的服务选择和组合问题。本文在对图数据进行聚类特征分析后，对制造网络结构进行分析研究，发现聚类形成的网络特征。提出了一种使用伪标签的深度制造云服务聚类模型（DSCPL），该模型将图拓扑和节点特征相结合，对具有相似属性的节点进行聚类。论文的具体贡献如下：1）定义一个参数非线性映射，将包含图结构（邻接信息）和节点信息（特征）的图空间嵌入到一个低维特征空间中。2)构建辅助目标分布实现自学习机制以适应聚类任务，3)实现无监督聚类方法。综合实验表明，该方法在公共数据集和模拟数据集上的聚类效果优于目前先进的深度聚类方法。