Next Point-of-interest (POI) recommendation has been widely used in real scenarios to predict the next possible location based on user behavior patterns. However, existing methods predominantly rely on spatio-temporal associations and check-in sequence relationships between users and POIs, which fall short for users with limited interactions with POIs. Moreover, user preferences are inherently multi-dimensional, rendering user selections often influenced by multiple factors such as location categories and multi-modal information. To mitigate these issues, we introduce a Multi-Modal Knowledge Graph Modeling of Multi-Dimensional User Preferences for Next-POI Recommendation (M4REC for short). First, we define a multi-modal knowledge graph to organize the relationships among users, locations, categories, and multimodal information. Subsequently, we use the multi-modal knowledge graph-based relation-aware network to derive comprehensive entity representations from the constructed knowledge graph. Next, employing the temporal knowledge prediction method, we predict the user’s next-POI category and next-POI. Finally, the final recommendation results are obtained by enhancing the corresponding location prediction scores through category semantics. Extensive experimentation conducted on real-world datasets validates the superiority of our proposed method over state-of-the-art competitors.