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Objective: Discussion based on neural networks in the 31P MR spectroscopy to distinguish hepatocellular carcinoma, normal liver and cirrhosis in value. Methods: Using self-organizing map neural network (SOM) analyse 66 data of 31P MRS, including hepatocellular carcinoma (13 samples), normal liver (16 samples) and liver cirrhosis (37 samples). Results: 31P MRS can be used for the diagnosis and differential diagnosis between hepatocellular carcinoma and liver cirrhosis nodules. The four experiments show that neural network model based on the 31P MR spectroscopy data analysis may increase diagnostic accuracy rate of hepatocellular carcinoma from 85.4% to 92.31%. Conclusion: 31P MRS data analysis based on neural network model provides a valuable diagnostic means of of hepatocellular carcinoma in vivo.