In order to improve fuel economy and prolong battery life of hydrogen fuel cell hybrid electric vehicle , three typical working conditions in "China heavy-duty commercial vehicle test cycle-truck" were selected to represent "urban", "suburban" and "expressway", respectively, and corresponding optimal energy management strategies were formulated; support vector machine optimized by genetic algorithm was used. GA-SVM algorithm identifies vehicle operating conditions and dynamically chooses corresponding energy management strategies to make them adaptive to selected representative conditions, thus reducing hydrogen consumption and prolonging battery life. The simulation results show that the equivalent hydrogen consumption of the energy management strategy based on GA-SVM was reduced by 7.78% and 1.31% respectively, compared with the energy management strategy based on condition-free identification and the energy management strategy based on SVM optimized by traditional algorithm, and the SOC change of storage battery was reduced, and the change was relatively stable, which is beneficial to prolonging battery life.