Multimodal　sentiment　analysis　aims　to　extract　and　integrate　information　from　different　modalities　to　accurately　identify　the　sentiment　expressed　in　multimodal　data.　How　to　effectively　capture　the　relevant　information　within　a　specific　modality　and　how　to　fully　exploit　the　complementary　information　among　multiple　modalities　are　two　major　challenges　in　multimodal　sentiment　analysis.　Traditional　approaches　fail　to　obtain　the　global　contextual　information　of　long　time-series　data　when　extracting　unimodal　temporal　features,　and　they　usually　fuse　the　features　from　multiple　modalities　with　the　same　method　and　ignore　the　correlation　between　different　modalities　when　modeling　inter-modal　interactions.　In　this　paper,　we　first　propose　an　Attentional　Temporal　Convolutional　Network　(ATCN)　to　extract　unimodal　temporal　features　for　enhancing　the　feature　representation　ability,　then　introduce　a　Multi-layer　Feature　Fusion　(MFF)　model　to　improve　the　effectiveness　of　multimodal　fusion,　which　fuses　the　different-level　features　by　different　methods　according　to　the　correlation　coefficient　between　the　features,　and　cross-modal　multi-head　attention　is　used　to　fully　explore　the　potential　relationship　between　the　low-level　features.　The　experimental　results　on　SIMS　and　CMU-MOSI　datasets　show　that　the　proposed　model　achieves　superior　performance　on　sentiment　analysis　tasks　compared　to　state-of-the-art　baselines.