To solve the energy diagnosed problem of boiler hot water heat supply, a energy consumption diagnosed method based on machine learning algorithm was proposed, firstly filtrating the data which has better energy-conservation performance from all data based on clustering or classification method. Then based on the regression model, the informant data had been tested has been test. though Through the four case study, these conclusion conclusions can be gained:1) The R value of model which is built by artificial neutral neural network(ANN), which was trained by Bayesian regularization method based on the data clustered by K-means algorithm was is up to the 0.976, 0.970 5, 0.921 4, 0.910 1; 2) though the test by the three data gather Model validated with 3 diagnosed datasets, the energy conservation ratio were are 10.7%, 17%, 4%, the accumulation error has been is up to the -149 498.67,-86 526,-4 052.27 kW, the effect of new model is better than before; 3) the artificial control of first heating supply is the mainly reason, which cased the high heating energy. The model based on the physical response between input and output variable, which has higher robustness in time series can be widely employed in energy consumption diagnosed of boiler hot water supply system,and by the developing of data technology, the model based on the data machine learning can supply some idea ideas for the similar system.