The　recommendation　system　aims　to　recommend　items　to　users　by　capturing　their　personalized　interests.　Traditional　recommendation　systems　typically　focus　on　modeling　target　behaviors　between　users　and　items.　However,　in　practical　application　scenarios,　various　types　of　behaviors　(e.g.,　click,　favorite,　purchase,　etc.)　occur　between　users　and　items.　Despite　recent　efforts　in　modeling　various　behavior　types,　multi-behavior　recommendation　still　faces　two　significant　challenges.　The　first　challenge　is　how　to　comprehensively　capture　the　complex　relationships　between　various　types　of　behaviors,　including　their　interest　differences　and　interest　commonalities.　The　second　challenge　is　how　to　solve　the　sparsity　of　target　behaviors　while　ensuring　the　authenticity　of　information　from　various　types　of　behaviors.　To　address　these　issues,　a　multi-behavior　recommendation　framework　based　on　Multi-View　Multi-Behavior　Interest　Learning　Network　and　Contrastive　Learning　(MMNCL)　is　proposed.　This　framework　includes　a　multi-view　multi-behavior　interest　learning　module　that　consists　of　two　submodules:　the　behavior　difference　aware　submodule,　which　captures　intra-behavior　interests　for　each　behavior　type　and　the　correlations　between　various　types　of　behaviors,　and　the　behavior　commonality　aware　submodule,　which　captures　the　information　of　interest　commonalities　between　various　types　of　behaviors.　Additionally,　a　multi-view　contrastive　learning　module　is　proposed　to　conduct　node　self-　discrimination,　ensuring　the　authenticity　of　information　integration　among　various　types　of　behaviors,　and　facilitating　an　effective　fusion　of　interest　differences　and　interest　commonalities.　Experimental　results　on　three　real-world　benchmark　datasets　demonstrate　the　effectiveness　of　MMNCL　and　its　advantages　over　other　state-of-the-art　recommendation　models.　Our　code　is　available　at　https://github.com/sujieyang/MMNCL.