To construct the diagnosis model of chronic heart failure (CHF) patients with blood stasis syndrome based on multi-system physical and chemical index information by multiple data statistics tools and neural network mining methods,and to explore the biological significance of the physical and chemical index information related to the blood stasis syndrome of chronic heart failure patients.Methods:Clinical epidemiological investigation of 100 patients with CHF treated in Chengdu Integrated TCM & Western Medical Hospital and Hangzhou Hospital of Traditional Chinese Medicine from March 2010 to August 2011 was conducted,and the four diagnosis information of the patients was collected.The physical and chemical indexes of the biological samples were also detected and analyzed.On the basis of analysis of difference index,the regression method and the neural network data mining method were used to analyze the data to form the multi-system physical and chemical index diagnosis model of the patients with chronic heart failure and blood stasis syndrome.Results:The study included 100 patients with heart failure,of which 37% were patients with blood stasis syndrome.The above method was used to screen out 20 items of multi-system physical and chemical index,and the data mining model of the neural network was conducted according to the order of significance of the items.The accuracy of the model was 75.4%,and the accuracy of the test sample was 82.4%.Conclusion:The neural network data mining method can be used in the modeling and analysis of CHF patients with the blood stasis syndrome based on clinical physical and chemical information data.The multi-system physical and chemical information closely related to blood stasis also provide reference for further understanding of pathophysiological mechanism of chronic heart failure and blood stasis syndrome.