Legged　animals　have　the　flexibility　to　change　their　gait　locomotion　pattern　strategies　under　different　environments　and　walking　speeds,　which　is　important　for　efficient　locomotion.　However,　legged　locomotion　is　currently　studied　as　a　fixed　set　of　pre-programmed　gait　patterns,　which　limits　locomotor　flexibility.　Moreover,　the　process　of　designing　multiple　coping　strategies　for　multiple　tasks　is　cumbersome.　In　this　study,　we　introduce　a　novel　framework　for　quadrupedal　locomotion　learning,　enabling　robots　to　swiftly　and　adaptably　transition　between　gait　patterns　tailored　to　various　environments.　In　addition,　we　introduce　an　environmental　feedback　framework　to　improve　the　quadrupedal　robot＇s　ability　to　perceive　external　environmental　features.　We　obtain　a　strategy　network　through　a　two-stage　training　and　deploy　it　to　real　robots.　Finally,　a　multi-strategy　controller　is　obtained,　which　enables　the　quadruped　robot　to　realize　flexible　strategy　shifts　and,　moreover,　efficient　traversal　of　various　complex　terrain　environments.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.