The effectiveness of English Language Teaching (ELT) materials significantly influences the learning outcomes of English as a Foreign Language (EFL) learners, positioning these resources as cardinal in the acquisition process. Despite this widely acknowledged significance, there remains a notable gap in research concerning the evaluation of key factors, such as syntactic complexity and text readability, which determine text difficulty. To address this gap, the present study evaluated the syntactic complexity and readability of a series of "Intensive Reading" textbooks widely used by English majors in China by adopting data mining technologies, including the L2 syntactic complexity analyzer (L2SCA) and six readability indicators. It also aimed to explore the impact of syntactic complexity as a predictor variable on text readability using multiple regression analysis. The results revealed that the selected series of textbooks demonstrated an increasing pattern of syntactic complexity and a decreasing pattern of text readability, indicating the appropriateness of the text difficulty level for this series. It was also found that clause per T-unit (C/T), clause per sentence (C/S), and dependent clauses per clause (DC/C) were the most significant predictors of readability among the 14 indicators of syntactic complexity. The analytical approach, which leverages data-mining technologies for ELT material evaluation, sets a precedent for future research. The insights gained from this study offer valuable implications for the development of ELT materials in China and other similar EFL contexts, helping to optimize learning by providing appropriately challenging content.