Piezoelectric　ceramics　have　good　electromechanical　coupling　characteristics　and　a　high　sensitivity　to　load.　One　typical　engineering　application　of　piezoelectric　ceramic　is　its　use　as　a　signal　source　for　Weigh-In-Motion　(WIM)　systems　in　road　traffic　monitoring.　However,　piezoelectric　ceramics　are　also　sensitive　to　temperature,　which　affects　their　measurement　accuracy.　In　this　study,　a　new　piezoelectric　ceramic　WIM　sensor　was　developed.　The　output　signals　of　sensors　under　different　loads　and　temperatures　were　obtained.　The　results　were　corrected　using　polynomial　regression　and　a　Genetic　Algorithm　Back　Propagation　(GA-BP)　neural　network　algorithm,　respectively.　The　results　show　that　the　GA-BP　neural　network　algorithm　had　a　better　effect　on　sensor　temperature　compensation.　Before　and　after　GA-BP　compensation,　the　maximum　relative　error　decreased　from　about　30%　to　less　than　4%.　The　sensitivity　coefficient　of　the　sensor　reduced　from　1.0192　x　10(-2)/degrees　C　to　1.896　x　10(-4)/degrees　C.　The　results　show　that　the　GA-BP　algorithm　greatly　reduced　the　influence　of　temperature　on　the　piezoelectric　ceramic　sensor　and　improved　its　temperature　stability　and　accuracy,　which　helped　improve　the　efficiency　of　clean-energy　harvesting　and　conversion.