With rapid urbanization and increasing vehicle density, traffic accidents have emerged as a significant public safety issue. However, existing methods are deficient in several ways: functional area analysis based on points-of-interest (POI) quantity cannot quantify the impact weights of land use features on accident occurrence. Relying solely on raw time series analysis cannot capture deep temporal dependencies, while focusing solely on local spatial features ignores cross-regional geographic correlation and global semantic dependency. Considering these factors, a traffic accident prediction method that integrates land use knowledge with multi-granularity temporal and spatial dependencies (MGST-FK) is proposed in this paper. The method develops a land use knowledge mining module to quantify the influence weights of land use feature variables on the probability of traffic accidents on different roads at different time periods. Furthermore, based on a multi-granularity joint computing concept, in the temporal dimension, the original time series is divided into three granularities to extract explicit and implicit temporal patterns. In the spatial dimension, a coarse-grained functional similarity graph is constructed to capture global semantic dependency. Experimental results on the real traffic dataset from Manhattan demonstrate the superiority of the method in enhancing traffic accident prediction performance compared to existing methods.