ATextCNN Model: A New Multi-classification Method for Police Situation

Abstract

The information age poses new challenges to the automatic and rapid classification of public security data. To get rid of the low-efficiency work mode of manual identification, perform rapid automatic classification of police situation, and improve the performance and accuracy of classifiers, many models and algorithms have been proposed. The traditional police situation classification methods almost always use traditional machine learning algorithms. The shortcomings of complex working mode, heavy workload, and poor learning effect have led to the rise of deep learning classification methods. This paper proposes the ATextCNN model, which introduces an attention mechanism in the Input Layer based on the TextCNN model, and uses a word filtering algorithm to calculate the contribution of words to filter nonsense words. The ATextCNN model develops a multi-classification model based on deep learning. We conduct experiments on two real data sets and divided data into nine categories. Experiments show that the ATextCNN model is better than the traditional classification methods in terms of accuracy and efficiency when performing multi-classification tasks, and is better than the state of the art method in practical application. Therefore, our method is more conducive to police situation identification and management.