TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing. Due to its remarkable guessing performance, the model has drawn considerable attention in password security research. However, through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information, we identified that the model fails to consider popular passwords and frequent substrings, and it uses overly broad personal information categories, with extensive duplicate statistics. To address these issues, we propose an improved password guessing model, TGI-FPR, which incorporates three semantic methods: (1) identification of popular passwords by generating top 300 lists from similar websites, (2) use of frequent substrings as new grammatical labels to capture finer-grained password structures, and (3) further subdivision of the six major categories of personal information. To evaluate the performance of the proposed model, we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I. The results indicate a 2.65% improvement in guessing accuracy.