To improve the recognition accuracy of human activity based on the smart mobile device, an Multiple Classifier Fusion Model for activity recognition (MCFM) based on high reliability weighting was proposed. According to the triaxial acceleration imformation obtained by different smart device with built-in acceleration sensor, those features of high correlation with human daily activities were extracted from the original acceleration as the input of MCFM. Then the three base classifiers of decision tree, Support Vector Machine (SVM) and Back Propagation (BP) neural network were trained for a new fusion classifier by using the High Reliability Weighted Voting (HRWV) algorithm. The experimental results show that the the proposed classifier fusion model can effectively improve the accuracy of human activity recognition, its average recognition accuracy of the five daily activities (stay, walk, run, stairs, downstairs) reaches 94.88%.