In-Depth Application of Neural Network in Software Information Classification and Identification

Neural network is a highly self-learning computing technique capable of handling nonlinear and multivariate data, which has great advantages in improving the efficiency of data processing. By constantly adjusting parameters, neural networks can effectively avoid possible errors in algorithm training. This paper aims to study the application of neural network in information classification. First, the paper introduces the artificial neuron model and its design method. Secondly, this paper deeply discusses the influence of the BP (Back Propagation) layer on the signal feature value extraction process and the reasons for reducing the recognition rate. Afterwards, this paper designs a software information classification and recognition system, and conducts a functional test on the recognition and classification performance of the system. The test results show that the memory usage of the classification information and recognition system is between 1% and 3%, which shows that the system is not bloated and can meet the hardware operation requirements. The application of neural network in software information classification and recognition shows that neural network has potential in solving common problems in the field of artificial intelligence. Neural network is a network composed of artificial neurons that simulate the way of human brain transmitting signals. It learns according to the training data and improves its accuracy over time. Neural network is usually composed of input layer, hidden layer and output layer. Each node (artificial neuron) transmits data through the connection weight and threshold with other nodes.