An energy consumption measuring method, comprising the following steps being performed by an energy consumption measuring system: acquiring motion data of a user within a time period; processing the acquired motion data to acquire a motion feature vector; comparing the acquired motion feature vector with a standard motion feature vector in a database so as to judge whether the motion mode of the user is matched with the database; acquiring an energy consumption value according to the data in the database if matching; and allowing the user to create a customized motion model and input a corresponding energy consumption value if not matching, characterized in that acquiring the motion feature vector comprises: partitioning the motion data into m data units in unit of certain time, each data unit contains n data points of each motion variable, wherein both m and n are natural numbers; for the m data units, calculating average values, variances and histogram slopes of the n data points of each motion variable in x-axis, y-axis and z-axis directions, respectively; performing Gaussian probability distribution calculation on the average values, variances and histogram slopes of the n data points of each motion variable of all the m data units in the x-axis, y-axis and z-axis directions to acquire discrete feature values and position feature values of a corresponding Gaussian distribution curve, and using a calculation result of least square fitting of the position feature values as a feature vector of the corresponding motion variable; and using a set of the feature vectors of the motion variables as the motion feature vector of the corresponding data unit. acquiring motion data of a user within a time period; processing the acquired motion data to acquire a motion feature vector; comparing the acquired motion feature vector with a standard motion feature vector in a database so as to judge whether the motion mode of the user is matched with the database; acquiring an energy consumption value according to the data in the database if matching; and allowing the user to create a customized motion model and input a corresponding energy consumption value if not matching, characterized in that acquiring the motion feature vector comprises: partitioning the motion data into m data units in unit of certain time, each data unit contains n data points of each motion variable, wherein both m and n are natural numbers; for the m data units, calculating average values, variances and histogram slopes of the n data points of each motion variable in x-axis, y-axis and z-axis directions, respectively; performing Gaussian probability distribution calculation on the average values, variances and histogram slopes of the n data points of each motion variable of all the m data units in the x-axis, y-axis and z-axis directions to acquire discrete feature values and position feature values of a corresponding Gaussian distribution curve, and using a calculation result of least square fitting of the position feature values as a feature vector of the corresponding motion variable; and using a set of the feature vectors of the motion variables as the motion feature vector of the corresponding data unit. partitioning the motion data into m data units in unit of certain time, each data unit contains n data points of each motion variable, wherein both m and n are natural numbers; for the m data units, calculating average values, variances and histogram slopes of the n data points of each motion variable in x-axis, y-axis and z-axis directions, respectively; performing Gaussian probability distribution calculation on the average values, variances and histogram slopes of the n data points of each motion variable of all the m data units in the x-axis, y-axis and z-axis directions to acquire discrete feature values and position feature values of a corresponding Gaussian distribution curve, and using a calculation result of least square fitting of the position feature values as a feature vector of the corresponding motion variable; and using a set of the feature vectors of the motion variables as the motion feature vector of the corresponding data unit.