Considering the low power consumption for successive indoor and outdoor scenes pervasive perception in complex and dynamic environment, a lightweight indoor and outdoor scene identification algorithm based on Support Vector Machine (SVM) multi-classifier was proposed, which can accurately distinguish the indoor and outdoor scenes with low power consumption. The algorithm adopted data mining method to obtain different characteristics in indoor and outdoor scenes from the sensors integrated in smart phones (such as visible light sensors, magnetic sensors, acceleration sensors, gyro sensors, and pressure sensors, etc.). It also made advantage of human behavior difference between indoor and outdoor scene. According to different time and weather conditions, the algorithm designed support vector machine multi-classifier to identify complex indoor and outdoor scenes based on the differences of human behavior in indoor and outdoor scene. The simulation results show that the proposed algorithm has good universality, and can determine the indoor and outdoor scenes with more than 95% accuracy, and only consumes less than 5 mW averaging power.