Hyperspectral　images　(HSIs)　contain　rich　spectral　information　and　spatial　information.　How　to　apply　these　two　information　types　and　fully　combine　the　correlation　between　them　remains　a　challenge　worthy　of　further　research　and　discussion.　In　this　study,　a　multi-branchmulti-scale　residual　fusion　network　(MB-MS-RFN)　for　HSI　classification　is　proposed.　First,　a　3D　multi-branch-multi-scale　convolution　residual　network,　which　can　acquire　image　features　of　different　scale　in　the　training　process　and　consider　the　correlation　between　spectral　information　and　spatial　information,　is　developed.　Instead　of　deepening　the　network,　the　multi-branch　structure　widens　the　network　horizontally　to　obtain　more　accurate　classification.　Finally,　the　different　levels　of　HSI　features　are　fused　to　obtain　better　classification　results.　Several　experiments　have　been　carried　out　to　verify　the　proposed　framework,　and　the　results　have　demonstrated　that　the　proposed　MB-MS-RFN　framework　can　improve　the　classification　performance　of　HSIs.　The　performance　of　the　MB-MS-RFN　was　evaluated　using　the　Indian　Pines,　Pavia　University,　and　Kennedy　Space　Center　datasets;　the　performances＇　overall　accuracies　were　99.66%,　99.92%,　and　99.97%,　respectively.　The　results　from　a　series　of　experiments　confirm　that　the　proposed　method　offers　several　advantages　in　classification　accuracy　compared　with　five　other　methods.　(C)　2021　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)