The current group behavior recognition method do not make full use of the group relational information, so that the group recognition accuracy cannot be effectively improved. Therefore, a deep neural network model based on the hierarchical relational module of Affinity Propagation (AP) algorithm was proposed, named Clustering Relational Network (CRN). First, Convolutional Neural Network (CNN) was used to extract scene features, and the regional feature clustering was used to extract person features in the scene. Second, the hierarchical relational network module of AP was adopted to extract group relational information. Finally, the individual feature sequences and group relational information were fused by Long Short-Term Memory (LSTM) network, and the final group recognition result was obtained. Compared with the Multi-Stream Convolutional Neural Network (MSCNN), CRN has the recognition accuracy improved by 5.39 and 3.33 percentage points on Volleyball dataset and Collective Activity dataset, respectively. Compared with the Confidence-Energy Recurrent Network (CERN), CRN has the recognition accuracy improved by 8.70 and 3.14 percentage points on Volleyball dataset and Collective dataset, respectively. Experimental results show that CRN has higher recognition accuracy in the group behavior recognition tasks.

Since the traditional Enterprise Resource Planning (ERP) system has low openness, low expansibility and high cost in current business environment, an ERP system modeling method based on Software-as-a-Service (SaaS) model was proposed. Firstly, a new primitive set called Unified Modeling Language (UML) profile was gotten by extending the primitives of UML. Secondly, an equivalent meta model was established and semantic unambiguity was ensured by Object Constraint Language (OCL). Finally, the cloud ERP was described by the model framework which was composed of application diagram, operation dictionary, physical diagram and topological diagram to transform the cloud ERP system into documents. The proposed method focused on the modular design and all stages adopted a unified visual meta-model. According to the requirements of modeling, the ERP model based on SaaS was successfully established on the Enterprise Architect (EA) platform by the proposed method and the effectiveness was verified. The theoretical analysis and modeling results show that the proposed method can ensure the interoperability and consistency between models and improve the scalability of ERP system.