The　prediction　of　trip　generation　is　an　essential　problem　for　effective　traffic　engineering　and　urban　management.　Traditional　methods　are　on　the　large　spatial　scale　(e.g.　Traffic　analysis　Zone,　TAZ),　based　on　the　single　source　and　fewer　types　data.　It　is　difficult　to　carry　out　refined　research　on　smaller　spatial　units,　due　to　the　high　aggregation　of　personal　trip　survey　data.　In　addition,　the　experience-based　models　cannot　easily　capture　complex　non-linear　relationship,　which　leads　to　lower　accuracy.　Multi-sources　data　provides　the　possibility　to　improve　the　prediction　accuracy　of　trip　generation.　Based　on　the　point　of　interest　data　(POI),　more　disaggregate　spatial　unit　are　subdivided,　and　grid-scale　spatial　correlations　are　taken　into　consideration.　This　paper　proposes　a　Convolutional　Neural　Network-Multidimensional　Long-short　term　memory　neural　network　(CNN-MDLSTM)　model　to　analyze　the　spatial　correlation　between　trip　generation　and　land　use　features,　capture　prominent　features　in　a　spatial　range　through　the　convolution　structure　and　describe　the　spatial　interaction　using　the　sequence　transfer　structure　of　Long-short　term　memory　neural　network　(LSTM).　The　deformed　Multi-Dimensional　Long-Short　Term　Memory　neural　network　(MDLSTM)　is　used　to　adapt　to　the　two-dimensional　spatial　relationship.　Through　case　analysis　and　comparative　analysis　between　models,　it　is　shown　that　CNN-MDLSTM　characterizes　the　quantitative　law　of　trip　generation　and　land　use　features　better　than　the　other　neural　network　models.　In　addition,　this　study　also　discusses　the　output　prediction　accuracy　of　different　land　use　grid　cells　and　the　impact　of　different　land　use　characteristics　on　the　prediction　accuracy.