The　nonlocal　integral　method　typically　requires　a　very　high　computing　cost　to　search　neighborhood　integration　points　for　calculating　the　nonlocal　variable,　which　limits　its　application　in　large-scale　problems.　This　paper　proposes　an　efficient　nonlocal　integral　method　based　on　the　octree　algorithm,　in　which　the　integration　point　information　is　stored　in　the　tree　data　structure　to　accelerate　the　search　task.　Firstly,　the　fundamental　principles　and　implementation　details　of　using　the　octree　algorithm　to　search　neighborhood　integration　points　are　described　in　detail.　Subsequently,　a　Mohr-Coulomb　nonlocal　damage　plastic　model　is　presented　as　the　application　object　of　the　proposed　method.　The　model　is　implemented　further　in　the　ABAQUS　using　the　octree-based　nonlocal　method　and　the　return　mapping　algorithm　enhanced　by　a　line　search　method.　Finally,　two　typical　boundary　value　problems　are　simulated　to　verify　the　effectiveness　and　to　assess　the　computational　efficiency　of　the　proposed　nonlocal　method.　For　the　given　test　environment,　the　octree　algorithm　can　achieve　up　to　100　times　speedup　at　the　integration　point　level　compared　to　the　traversal　algorithm,　and　the　developed　efficient　nonlocal　method　can　achieve　up　to　7.9　times　speedup　at　the　boundary　value　problem　level　compared　to　the　original　nonlocal　method.