A　multi-scale　feature　fusion　convolutional　neural　network-based　SSVEP　signal　classification　and　recognition　method　is　proposed　to　solve　the　problems　of　low　classification　accuracy,　inadequate　feature　extraction,　complex　and　time-consuming　methods　of　traditional　steady-state　visual　evoked　potential　(SSVEP-MF)　signal　target　recognition　methods.　Firstly,　the　wavelet　transform　is　used　to　integrate　the　multi-channel　SSVEP　signals　into　two-dimensional　images　as　the　input　sample　set;　secondly,　a　multi-scale　feature　fusion　convolutional　neural　network　model　(MFCNN)　is　established,　which　uses　a　three-layer　two-dimensional　convolutional　kernel　to　achieve　sufficient　extraction　of　features　at　different　scales　of　image　samples,　constructs　multi-scale　feature　fusion　units　to　fuse　features　at　different　levels,　and　completes　the　training　of　the　model　through　operations　such　as　full　connectivity;　finally,　the　sample　set　is　input　to　the　MFCNN　model　to　achieve　adaptive　extraction　of　EEG　signal　features　and　end-to-end　classification.　The　proposed　SSVEP-MF　method　can　fully　extract　the　features　at　each　level　of　the　signal,　achieve　effective　recognition　of　SSVEP　signals　under　short-time　visual　stimulation,　and　have　high　target　recognition　efficiency.　The　experimental　results　show　that　the　recognition　accuracy　of　the　proposed　method　is　improved　by　18.57%,　20.08%　and　7.03%,　respectively,　compared　with　the　traditional　power　spectral　density　analysis　method,　typical　correlation　analysis　method　and　common　convolutional　structure　method　at　1　s　stimulus　duration,　which　effectively　improves　the　signal　recognition　performance　of　brain-machine　interface　based　on　the　steady-state　visual　evoked　potential　paradigm.　©　2022,　Editorial　Office　of　Journal　of　Xi＇an　Jiaotong　University.　All　right　reserved.