A Study on Sentencing for Traffic Offences Based on the Decision Tree Algorithm

Traffic offences are characterised by high complexity and low sentencing accuracy in the ordinary prosecution sentencing process. Decision tree algorithm is a more effective sentencing accuracy and high fairness of the intelligent prosecution sentencing tool. The algorithm analyses the sentencing situation by establishing a data mining model, analysing and data crawling a large number of cases and judgments. A data table of traffic offence features is established by keyword search and feature information extraction to form a jurisprudence database. Using python language and related tool libraries, the factors of the formation of traffic offence are considered comprehensively for sentencing prediction, which is used as an auxiliary tool for sentencing. Through the experiment for data collection and analysis, the data structure model and algorithm model are constructed, and the expansion analysis of the algorithm model is achieved to realise the intelligent sentencing process for traffic crime.