Algorithm Research on Semantic Analysis Technology in Communication Content Security Monitoring

With the widespread application of communication networks, how to effectively monitor security risks in communication content has become an urgent task. Traditional security monitoring methods rely on rule matching and are difficult to cope with complex and changing contexts and content. To this end, this paper proposes an adaptive communication content security monitoring algorithm (ADSA) based on deep semantic analysis. The algorithm combines DNN and RNN, can understand the communication content from a semantic level, and dynamically adjust the monitoring strategy using an adaptive weighting mechanism. The model extracts text features through deep semantic analysis, uses context modeling to capture information flow trends, and monitors potential security risks in communication content in real time. In the experiment, the ADSA model performed well in a variety of communication environments. Compared with the traditional keyword-based monitoring method, the accuracy rate increased by about 20%, the recall rate increased by about 15%, and the false alarm rate was significantly reduced. Experimental data and simulation results prove the effectiveness of the model in practical applications. It can quickly and accurately detect malicious content and abnormal behavior, and provide efficient technical support for content security monitoring in communication networks.