Big data technology can play a significant role in exploring and analyzing classical Chinese literature and in enhancing our understanding and promotion of traditional culture. Analyzing psycholinguistic words used in ancient people’s self-expression texts is a good way to understand their psychological state. Based on the classical Chinese segmentation methods used by such dictionaries as CCIDict and CC-LIWC, this paper proposed a word segmentation algorithm that can better cover the ancient Chinese vocabulary used in imperial edicts. We used this algorithm to calculate the psycholinguistic words in imperial edicts of the Western and Eastern Jin Dynasties (265–420). We firstly collected 613 edicts from 18 emperors of the Western and Eastern Jin Dynasties, with a total word count of more than 45,000. After being analyzed and calculated by the dictionary-based classical Chinese word segmentation algorithm, all these words were divided into 78 categories of psycholinguistic words. By comparing the frequencies of such word categories in imperial edicts of the Western Jin (265–317) and the Eastern Jin (317–420), we found significant differences in the following five word categories: personal pronouns (p = 0.027), modal particles (p = 0.034), social process words (p = 0.016), difference words (p = 0.016), and time words (p = 0.043). Based on differences in these five categories, we analyzed the psychological changes of the Western Jin and Eastern Jin emperors. This paper thereby verified the applicability and feasibility of the dictionary-based classical Chinese word segmentation algorithm.