Liangliang Zhou
Department of policy and law, Party School of Cangzhou municipal Party committee of CPC, Cangzhou, 061000, China



In order to solve the problem of long execution time of traditional legal text classification, this paper designs a legal text classification method based on machine learning. Based on the combination of support vector machine (SVM) and naive Bayes classifier in machine learning, the feature value of legal text is filtered, the legal text classification matrix is established, and the legal text classification results are accessed directly through embedded system to realize the legal text classification. The experimental results show that the implementation time of the design method is more than twice as fast as that of the control group, which can solve the problem of long implementation time of the traditional method. AI and machine learning have the potential to transform the delivery of healthcare. However, creating decision support systems based on machine learning requires more than just a technological undertaking. As a result, bioethical standards must be considered. As AI and machine learning progress, bioethical frameworks must be adapted to solve the difficulties that these growing systems may bring, and the creation of these automated systems also has to be tailored to embrace bioethical concepts.