A metaphor detection model based on linguistic multi-incongruity was proposed to tackle the metaphor occurrence problem caused by the incongruity between the target sentence meaning and the core meaning of the target word in a specific context where a target word has multiple semantic meanings （polysemy）， which is ignored by the existing metaphor detection research. Firstly， in the feature encoding module， two separate encoders were employed to encode the feature information such as the target sentence meaning， the core meaning of the target word， and its contextual meaning. Then， in the multi-incongruity modeling module， three linguistic methods — Selectional Preference Violation （SPV）， Metaphor Identification Procedure （MIP）， and Semantics Usage Comparison （SUC） — were utilized to conduct unified modeling of incongruity features. Finally， metaphor detection was performed through a metaphor identification module. Furthermore， to validate Chinese metaphor detection performance， a Chinese word-level metaphor detection dataset named META-ZH was constructed through a data annotation method of combining LoRA （Low-Rank Adaptation） fine-tuned Large Language Model （LLM） with manual correction. Experimental results show that the proposed model achieves F1 values improvement of 0.8， 1.3， 1.5， and 2.3 percentage points， respectively， compared to the optimal baseline model on the VUA All， VUA Verb， MOH-X， and META-ZH metaphor detection datasets. It can be seen that the proposed model enhances performance in metaphor detection by fully utilizing linguistic multi-incongruity.