Research on the Application of AI Intelligent Model of Computer Deep Learning in Natural Language Processing

The relationships between entities in a document are extracted according to natural language processing methods. Deep neural network is used to recognize the required multi-label text. According to the general specification, the system is optimized, and the design and implementation of the system are obtained. This project explores four major NLP modes such as ALBERT, RNN Search, BERT-CRF, Text ING based on the high-performance hardware of the Centeno platform. According to the element relation, tree structure and network structure, a general MNet construction method is proposed. The extracted correlation information is used to determine whether the matching conditions of each security requirement template are established, and then the final set of security requirement templates is screened. The extracted security requirements are modeled and instantiated in this way. Simulation results show that the model can deal with semantic dependency and human-computer interaction in complex systems. By analyzing the semantics of the operation interface in SCADA system, it is transformed into a general MNet construction, which lays a foundation for realizing the semantic analysis of users.