Chinese idioms, as an essential part of Chinese writing, possess concise expressiveness and profound cultural significance. They are typically phrases or short sentences that have become fixed through long-term use, with diverse origins and relatively stable meanings. However, due to the pictographic nature of Chinese characters and the historical evolution of Chinese vocabulary and semantics, there is often a discrepancy between the literal and actual meanings of idioms, which exhibits a unique non-compositional characteristic. This feature makes idioms prone to misuse of idioms in practice, with research showing that certain idioms are misused at a rate as high as 98.6%. Unlike in other languages, the misuse of Chinese idioms does not typically result in lexical or grammatical errors, which makes traditional spelling and grammar error detection methods ineffective at identifying idiom misuse. An intuitive approach is to incorporate the interpretations of idioms into the model, but simply combining these interpretations can lead to problems such as excessively long sentences that are hard to process and noise in knowledge. To address this, this study proposes a novel model that uses levitating knowledge injection to incorporate idiom interpretations. This model introduces learnable weight factors to control the injection process and explores effective strategies for knowledge infusion. To validate the model’s effectiveness, a dataset specifically for diagnosing the misuse of Chinese idioms is created. Experimental results show that the model achieves optimal performance across all test sets, particularly in complex scenarios involving long texts and multiple idioms, where its performance improves by 12.4%–13.9% compared to the baseline model. At the same time, training speed increases by 30%–40%, and testing speed is improved by 90%. These results demonstrate that the proposed model not only effectively integrates the interpretative features of idioms but also significantly reduces the negative impact of interpretation concatenation on the model’s processing capacity and efficiency, thus enhancing the performance of Chinese idiom misuse diagnosis and strengthening the model’s ability to handle complex scenarios with multiple idioms and lengthy interpretations.