The invention discloses a daily activity identifying method with exercising and physiology sensing data fused. According to the method, an smart phone and a wearable physiology sensing device are used for collecting exercising sensing data and physiology sensing data; then time domain and frequency domain statistical features and nonlinear features are extracted respectively, and feature selection is carried out through a sequence floating forward selection method; through a support vector machine and a Gaussian mixture model, activity identifying submodels of the exercising sensing data and the physiology sensing data are trained respectively; and finally the submodels are subjected to weighting integration through a fractional order fusion method, and a final daily activity classification model is obtained. The exercising sensing data and the physiology sensing data are fused, the accuracy of daily activity identifying can be improved, and the daily activity identifying method has wide application prospect in the fields of intelligent families, medial health care, old people assisting and the like.