We　present　a　sparse　Bayesian　reconstruction　method　based　on　multiple　types　of　a　priori　information　for　multispectral　bioluminescence　tomography　(BLT).　In　the　Bayesian　approach,　five　kinds　of　a　priori　information　are　incorporated,　reducing　the　ill-posedness　of　BLT.　Specifically,　source　sparsity　characteristic　is　considered　to　promote　reconstruction　results.　Considering　the　computational　burden　in　the　multispectral　case,　a　series　of　strategies　is　adopted　to　improve　computational　efficiency,　such　as　optimal　permissible　source　region　strategy　and　node　model　of　the　finite　element　method.　The　performance　of　the　proposed　algorithm　is　validated　by　a　heterogeneous　three-dimensional　(3D)　micron　scale　computed　tomography　atlas　and　a　mouse-shaped　phantom.　Reconstructed　results　demonstrate　the　feasibility　and　effectiveness　of　the　proposed　algorithm.