Graded reading is one of the important ways of English learning. How to automatically judge and grade the difficulty of English reading corpus is of great significance for precision teaching and personalized learning. However, the current rule-based readability assessment methods have some limitations, such as low efficiency and poor accuracy. Specially, these traditional methods usually lack semantics that is crucial for students to understand the reading material. Meanwhile, they are difficult to be mapped to the difficulty level, which is not conducive to flexible application in actual personalized teaching. In this study, a method for grading the difficulty of English reading corpus is proposed. This approach makes use of a pre-trained language model and feature fusion embedding to make the most of multi-feature data when training. First, based on linguists' evaluations of the variables influencing the difficulty of English reading corpus, three primary statistical features—sentence length, word length, and the number of prepositions—are taken into consideration. Then, the semantic features and part-of-speech features of the text are learned by pre-trained language model and LSTM respectively to capture polysemy features and fine-grained semantic representations that are difficult to represent with traditional models. Finally, multi-feature embedding extractions are fused to grade the difficulty of English reading corpus. Extensive experimental comparisons on a self-built dataset and two datasets that are freely accessible with various models indicate that our method outperform the others in the task of grading the difficulty of English reading corpora.