Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models

Abstract

In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.