Predicting　whether　crime　events　will　occur　indifferent　areas　(framed　as　a　classification　task)　is　atypical　spatio-temporal　data　mining　problem,　crucial　for　both　urban　management　and　public　safety.　Contemporary　crime　occurrence　prediction　models　predominantly　leverage　deep　learning　techniques,　focusing　on　capturing　the　spatio-temporal　dependencies　within　crime　data.　Analysis　of　crime　data　reveals　correlations　among　different　crime　types,　indicating　shared　change　patterns.　Leveraging　these　correlations　among　crime　types　significantly　enhances　the　accuracy　of　crime　occurrence　predictions.　Nevertheless,　existing　crime　occurrence　prediction　models　frequently　overlook　the　utilization　of　these　type　correlations.　To　solve　this　problem,　we　propose　a　new　crime　occurrence　prediction　model　with　multi-type　crime　correlation　learning:　the　Multi-type　Relations　Aware　Graph　Neural　Networks　(MRAGNN).　The　model　dynamically　constructs　a　spatial/type　graph　structure　of　crime　data　and　employs　dynamic　graph　networks　to　capture　both　spatio-temporal　and　type-temporal　dependencies　within　the　data.　We　introduce　a　cross-modal　gated　fusion　mechanism　to　fuse　the　representations　of　two　dependencies.　Furthermore,　we　develop　an　improved　multi-label　classification　focal　loss　to　address　the　challenges　posed　by　the　imbalance　in　crime　occurrence　data　on　classification　results.　Experimental　results　validate　that　our　model　outperforms　state-of-the-art　(SOTA)　methods　in　crime　occurrence　prediction.