Abstract: Metaphor recognition is one of the essential tasks of semantic understanding in natural language processing, aiming to identify whether one concept is viewed in terms of the properties and characteristics of the other. Since pure neural network methods are restricted by the scale of datasets and the sparsity of human annotations, recent researchers working on metaphor recognition explore how to combine the knowledge in other tasks and coarse-grained syntactic knowledge with neural network models, obtaining more effective feature vectors for sequence coding and modeling in text. However, the existing methods ignore the word sense knowledge and fine-grained syntactic knowledge, resulting in the problem of low utilization of external knowledge and the difficulty to model complex context. Aiming at the above issues, a knowledge-enhanced graph encoding method (KEG) for metaphor detection in text is proposed. This method consists of three parts. In the encoding layer, the sense vector is trained using the word sense knowledge, combined with the context vector generated by the pre-training model to enhance the semantic representation. In the graph layer, the information graph is constructed using fine-grained syntactic knowledge, and then the fine-grained context is calculated. The layer is combined with the graph recurrent neural network, whose state transition is carried out iteratively to obtain the node vector and the global vector representing the word and the sentence, respectively, to realize the efficient modeling of the complex context. In the decoding layer, conditional random fields are used to decode the sequence tags following the sequence labeling architecture. Experimental results show that this method effectively improves the performance on four international public datasets.