Construction of English Semantic Analysis System Based on AI Technology

With the rapid development of information technology and the deepening of artificial intelligence research, English has become more and more important as an important tool for human communication. In this context, it is of great value to develop a semantic analysis system based on AI technology. This paper first introduces the relevant knowledge of text mining and language recognition, then elaborates on the key methods such as neural network and ontology theory, and improves and optimizes the translation rule design module of the traditional vocabulary. Next, this paper describes in detail the construction idea and implementation process of the artificial intelligence-based voice data set label model based on machine learning, and gives the implementation scheme, and conducts the performance test of the system. The test results show that the semantic accuracy of System A shows a downward trend, which indicates that when it comes to a wider range of data, System A gradually weakens in terms of semantic understanding ability, resulting in a decline in accuracy. System B maintains a high level of semantic accuracy in different data ranges, and has a downward trend in general. This shows that System B has a good semantic understanding ability, but in a specific range of data, there may still be a certain degree of accuracy decline. As the range of data increases, the semantic accuracy of System C also decreases, and changes relatively slowly. This means that System C will have a certain loss of accuracy when processing data of different sizes, but the overall trend of change is relatively stable. Such a system will undoubtedly bring great progress and innovation to the field of semantic analysis and speech processing.