Sentiment　analysis　plays　an　indispensable　part　in　human-computer　interaction.　Multimodal　sentiment　analysis　can　overcome　the　shortcomings　of　unimodal　sentiment　analysis　by　fusing　multimodal　data.　However,　how　to　extracte　improved　feature　representations　and　how　to　execute　effective　modality　fusion　are　two　crucial　problems　in　multimodal　sentiment　analysis.　Traditional　work　uses　simple　sub-models　for　feature　extraction,　and　they　ignore　features　of　different　scales　and　fuse　different　modalities　of　data　equally,　making　it　easier　to　incorporate　extraneous　information　and　affect　analysis　accuracy.　In　this　paper,　we　propose　a　Multimodal　Sentiment　Analysis　model　based　on　Multi-scale　feature　extraction　and　Multi-task　learning　(M(3)SA).　First,　we　propose　a　multi-scale　feature　extraction　method　that　models　the　outputs　of　different　hidden　layers　with　the　method　of　channel　attention.　Second,　a　multimodal　fusion　strategy　based　on　the　key　modality　is　proposed,　which　utilizes　the　attention　mechanism　to　raise　the　proportion　of　the　key　modality　and　mines　the　relationship　between　the　key　modality　and　other　modalities.　Finally,　we　use　the　multi-task　learning　approach　to　train　the　proposed　model,　ensuring　that　the　model　can　learn　better　feature　representations.　Experimental　results　on　two　publicly　available　multimodal　sentiment　analysis　datasets　demonstrate　that　the　proposed　method　is　effective　and　that　the　proposed　model　outperforms　baselines.