As　the　basis　of　robot　kinematics,　path　planning　occupies　an　important　position　in　artificial　intelligence　and　other　fields.　However,　not　many　researches　have　focus　on　the　importance　of　generating　waypoints　in　path　planning.　In　order　to　accurately　find　the　most　efficient　moving　path,　we　proposes　a　path　planning　method　based　on　Naive　Bayes　Classifier　and　CNN　to　improve　A∗　algorithm,　which　is　a　search-based　algorithm.　It　first　constructs　a　cost　map　of　the　space　where　the　target　object　is　located,　and　obtains　the　starting　point,　ending　point　with　path　contour.　Next,　we　obtain　the　contours　of　obstacles　to　calculate　the　size,　use　Naive　Bayes　to　realize　the　mapping,　and　update　the　cost　map　faster　instead　of　the　classifers　with　network　structure　that　need　supervised　training.　After　that,　we　use　CNN　to　find　key　waypoints,　eliminate　redundant　waypoints　and　calculate　the　robot　gait.　Finally　the　path　planning　of　the　robot　is　realized.　Experiments　were　carried　out　in　the　simulation　environment　Gazebo　and　the　real　space　respectively,　and　the　results　showed　the　effectiveness　and　feasibility　of　the　method.　　©　2022　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.