developed a database containing 629 English compound words with such ratings [11]. Similarly, [PERSON] et al. amassed familiarity ratings for 2423 single-character simplified Chinese words [12]; [PERSON] et al. described 1300 native Mandarin Chinese speakers' familiarity norms regarding 20,275 two-character, 1231 three-character, and 2819 four-character simplified Chinese words [9]. However, this compilation task is time intensive and contingent on raters' experience; these aspects greatly limit its applicability to teaching and learning. Researchers have thus recently sought to extrapolate lexical psycholinguistic properties using advanced word representation and machine learning technologies. The automated extrapolation paradigm takes lexical knowledge or semantic information as word representation features. A mathematical method is next adopted to estimate the associations between these features and words' psycholinguistic property ratings. Estimation can involve either similarity-based or regression-based models. [PERSON] et al. reported French semantic similarity norms for 630 word pairs with varying levels of similarity and associated concreteness [13]. When designing a similarity-based model to identify words' familiarity scores in relation to their nearest neighbors, each word can be projected onto a high-dimensional semantic space based on contextual counts in a given corpus [14; 15]. Likewise, when evaluating word representation with lexical and semantic knowledge, regression models can be constructed from part of a psycholinguistic database based on human judgment ratings. The remaining database can then be tested with these models [16; 17; 18; 19]. However, no approaches exist to reliably assess familiarity ratings for domain concepts that involve compound words or phrases and include rich semantic information. Authors also have not fully considered regression models' interpretability for predicting domain concept familiarity. Advanced machine learning technologies can unearth the details of domain concepts' familiarity ratings. Associated findings will reinforce the trustworthiness of familiarity rating prediction. We focused on domain concept familiarity in this study, referring to the understanding of domain-specific (or domain-related) terms. This type of familiarity indicates concepts' complexity. Students must possess a clear understanding, or even mastery, of domain concepts to use them well while communicating. Students' familiarity with these concepts partly captures their learning engagement and effectiveness. We employed interpretable machine learning techniques to predict familiarity ratings for domain concepts. The findings offer quantitative insight into domain concept familiarity. In brief, our study was rooted in two aims: (1) to establish a robust regression model that can predict domain concepts' familiarity scores based on lexical features; and (2) to investigate which lexical factors contribute to familiarity prediction in light of explainable artificial intelligence. Three research questions (RQs) guided this effort. The results offer implications for Chinese lexical processing. Our findings can also aid instructors and designers in improving students' learning experiences and evaluating academic performance. **RQ1:**: What are the characteristics of lexical features within domain concepts? **RQ2:**: Can lexical features derived from domain concepts reliably predict accompanying familiarity ratings? **RQ2(a):**: Which tree-based regression model is ideal for predicting familiarity ratings? **RQ2(b):**: How accurately does the best regression model predict familiarity ratings? **RQ3:**: What are the most predictive lexical features from an interpretable machine learning perspective? How do lexical features affect the prediction of domain concepts' familiarity ratings?