中日现代语言通用汉字词各义项在两种语言中的使用情况一直是学界关注的难题。基于高频中日同形词的研究结果表明，利用BERT词向量技术对日语目标词义项统计的平均准确率达到了90%，最高达到97%；对汉语目标词义项统计的平均准确率达到了88.3%，最高也达到97%，利用词向量技术对中日汉字词汇语义开展计量研究具备可行性。同时研究还发现，传统词典义项设立的科学性、例句规范性和句长等因素都会对基于词向量的语义分析产生影响。

The sense frequency and distribution of Chinese characters commonly used in modern languages of China and Japan have always been a concern of the academic community, as well as a problem in the comparative study of the two languages. Using neural-network-based word embedding as a tool, this paper selects 10 high-frequency homographs with rich meanings as target words to conduct exploratory research on this issue. Research result shows that the sense classification accuracy of Japanese target words based on BERT word embedding reaches 90% on average and 97% at the highest; The average accuracy of Chinese target words has reached 88.3% with the highest also at 97%. It is feasible to use word embedding to carry out quantitative research on the semantics of Chinese and Japanese words. The research also reveals that, among other factors, the scientificity and rationality of word sense induction in traditional dictionaries, the length of dictionary sample sentences, the standardization of corpus sample sentences, the accuracy of sample sentence extraction, and the length of corpus sample sentences have an impact on the semantic analysis based on word embedding.