Previous　traffic　flow　prediction　studies　have　utilized　spatio-temporal　neural　networks　combined　with　the　multi-task　learning　framework　to　seek　complementary　information　for　enhancing　prediction　performance.　However,　the　existing　methods　still　face　two　challenges:　they　fail　to　capture　global　interaction　patterns　between　regions　and　lack　consideration　for　inter-correlations　within　interaction　patterns.　To　solve　these　issues,　we　propose　a　novel　multi-task　spatio-temporal　fully　convolutional　model　named　MSTFCM.　First,　the　model　includes　the　interaction　tensor　and　raster　tensor　as　task　inputs,　where　the　interaction　tensor　extends　the　raster　tensor　by　incorporating　global　interaction　patterns　between　regions.　Second,　a　multi-task　framework　combined　spatio-temporal　convolutional　block　was　used　to　learn　generalized　features　and　interaction　features.　A　channel　spatio-temporal　attention　is　added　to　adaptively　adjust　feature　weights　and　capture　inter-correlations.　To　train　the　MSTFCM,　the　uncertainty　loss　was　designed　as　the　learnable　loss　functions,　which　capture　various　flow　fluctuations,　to　facilitate　multi-task　optimization.　The　proposed　model　was　validated　on　two　real-world　traffic　datasets　collected　in　Xiamen,　China.　Experimental　results　showed　that　MSTFCM　outperformed　nine　baselines　in　one-step　and　multi-step　prediction,　with　slower　performance　degradation　as　predicted　time　intervals　and　steps　increased.　We　further　validated　the　model＇s　effectiveness　through　designed　variants　and　visualization　results.