Abstract: In recent years, traditional violent crimes and adult crimes in China have shown a downward trend. However, the types of crimes are endless. In order to effectively improve the ability of crime prediction in the public security practice and combat various types of illegal and criminal events, a new crime prediction model is proposed for crime data in this paper. The crime data are classified by density clustering analysis method, then the data is dimension-reduced to extract key attributes to generate feature data, and then the feature data are weighted and optimized, and the data are learned by the way of machine learning to predict the type of crime. The experimental results show that compared with the traditional methods, the proposed method has better prediction effect, providing a new path support for the detection and prevention of similar cases in the public security practice.

Key words: crime prediction, feature data, weighted optimization, machine learning