Stereo　matching　is　a　challenging　problem　in　computer　vision.　An　excellent　matching　cost　computation　method　is　useful　for　enhancing　stereo　matching　performance.　Traditional　matching　cost　computation　is　lack　of　robustness.　In　this　paper,　we　propose　a　crop-based　multi-branch　convolution　neural　network　(CBMBNet)　for　robust　matching　cost　computation.　We　employ　ResNeXt　block　for　feature　extraction　and　introduce　a　new　cropbased　multi-branch　network　structure　to　enhance　the　accuracy　of　matching.　Several　post-processing　techniques　are　used　further　to　enhance　disparity　map　equality.　The　experimental　results　show　that　the　proposed　CBMBNet　can　reduce　error　rates　than　MC-CNN-fst　and　MC-CNN-acrt　approaches　based　on　Middlebury　stereo　data　set.