Hyperspectral　images　can　be　widely　used　in　many　fields　due　to　their　high　information　richness.　However,　costly　and　complex　imaging　spectrometers　limit　its　growth.　Hyperspectral　reconstruction　aims　to　obtain　the　corresponding　hyperspectral　image　from　the　multispectral　image,　to　reduce　the　acquisition　cost　of　hyperspectral　images.　At　present,　the　related　works　mainly　use　methods　based　on　deep　learning,　and　they　have　achieved　good　results.　However,　how　to　fully　extract　the　global　spectral　and　spatial　features　of　hyperspectral　images　is　still　not　well　solved.　To　address　these　issues,　we　propose　an　Attention　and　Transformer　Complementary　Fusion　Network　(ATCFNet),　which　is　composed　of　three　modules:　Multi-angle　Input　Image　Processing　(MIIP),　Deep　Feature　Extraction　(DFE)　and　Hyperspectral　Reconstruction　(HR)　modules.　Within　the　DFE　module,　an　improved　Transformer　module　and　a　novel　Multi-scale　Spatial　Attention　(MSA)　module　are　proposed　to　extract　the　global　spectral　relationship　and　the　spatial　features　of　the　hyperspectral　images,　respectively.　Moreover,　the　MIIP　module　is　proposed　to　extract　the　features　of　input　multispectral　images　more　effectively　and　comprehensively.　To　verify　these,　we　compare　the　proposed　ATCFNet　with　other　excellent　reconstruction　methods　on　three　hyperspectral　image　datasets.　The　results　show　that　our　method　achieves　the　best　results　in　these　datasets.　Code　is　publicly　available　at　https://github.com/kidder314/ATCFNet.