Research on sensitive text classification detection and classification based on improved artificial neural network

With the rapid development of information technology, a large number of sensitive words have appeared, which has brought great harm to network security and social stability. Therefore, how to identify and classify these sensitive information accurately has become an important issue. Combined with the improved artificial network algorithm, an adaptive classification algorithm is proposed in the experiment, which can provide local intelligent classification service according to the classification results. At the same time, the algorithm transforms the clustering model structure of the traditional network algorithm, introduces the classification information, so that it can be applied to the classification problem, and expands the application field of the data field theory. The experimental results show that: (1) From the experimental results of different text quantities, the SOM algorithm assigns the classification task to different levels of nodes, and realizes the modularization of detection. (2) The overall mean results show that the highest recall rate is 87%, which has met the basic grading criteria, and the detection accuracy of sensitive words will also be improved. (3) The experimental results show that the algorithm can accurately classify the sensitive and speed up the parameter optimization, and is superior to the comparison algorithm in many indicators. (4) From the simulation results, compared with the traditional neural network algorithm, the precision and recall of the algorithm are maintained at more than 90% and the loss is less than 0.11.

Automated summary

With the rapid development of information technology, the emergence of sensitive words has posed a great threat to network security and social stability. This experiment proposes an adaptive classification algorithm that improves the network algorithm to accurately identify and classify sensitive information. The experimental results show positive outcomes.