Academic formulaic language is multi-word combinations with specific functions and semantics, which are important to improve the idiomaticity, fluency and logic of machine translation, intelligent question answering, automatic summarization, etc. In order to narrow the search range of the corpus and extract academic formulaic language more efficiently, this paper proposes a prediction model of academic formulaic language based on multi-feature fusion. The semantic features and part-of-speech features of the academic formulaic language are extracted separately, and then the late fusion method is used to learn multiple features and predict whether formulaic language is included in the sentence. Experimental results show that the late fusion method based on part-of-speech features and semantic features has the best predictive effect among the four fusion methods, which lays the foundation for further efficient recognition of academic formulaic language.