Design and Implementation of Police Equipment Knowledge Query System

In the field of public security,the standardized use of police equipment can better assist the public security police in performing their duties.With the advancement of science and technology of the times,police equipment is also constantly developing,and more and more new types of police equipment have appeared.Nowadays,there are a large number and variety of police equipment,and public security police are facing the challenge of mastering and updating equipment knowledge.This article builds a knowledge base of police equipment based on the knowledge of opening source data on the Internet,uses a variety of databases to store knowledge,and presents knowledge of police equipment in the formof knowledge queries,innovatively applying the concept of knowledge base to police Knowledge of equipment in science.Knowledge is presented in three modules:encyclopedia knowledge,question answering knowledge,and product knowledge,which can answer the description questions and experience questions of police equipment.The query accuracy rate for the physical problems of police equipment is 70%,and the query accuracy rate of the experience problems of police equipment is 90%.The design and implementation of the system solves the problem of knowledge fusion in the field of police equipment in opening source data,and at the same time can help the police to sort out the knowledge structure of police equipment,fully understand the police equipment,and better serve public security Field related work.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.