The　core　of　driving　force　control　strategy　is　the　research　core　of　electric　vehicle　driven　by　wheel　motor.　In　order　to　give　full　play　to　the　advantages　of　wheel　motor　drive　system,　a　reasonable　control　strategy　must　be　designed.　In　this　paper,　the　stability　of　the　vehicle　is　determined　based　on　the　sideslip　angle　of　the　center　of　mass　of　the　vehicle.　Through　the　speed　and　steering　wheel　angle,　the　actual　yaw　rate　of　the　vehicle　at　a　certain　speed　and　the　theoretical　yaw　rate　at　this　speed　are　determined　as　the　control　objectives.　The　PID　control　is　used　to　distribute　the　four-wheel　brake　pressure,　generate　additional　yaw　moment　to　adjust　the　vehicle　attitude;　and　the　traditional　PID　control　is　used　to　control　the　vehicle　attitude.　In　contrast,　fuzzy　control　using　human　expert　control　experience,　for　the　control　of　nonlinear　and　complex　objects　shows　the　advantages　of　good　robustness　and　high　control　performance.　Therefore,　on　the　basis　of　PID,　through　neural　network　learning,　fuzzy　neural　network　is　used　to　optimize　the　output　fuzzy　rules　and　membership　function　to　control　the　vehicle.　The　whole　process　is　carried　out　with　CarSim　and　Simulink　Simulation　is　carried　out　to　verify　the　control　optimization　process.