The physical and chemical indicators and heavy metal content in the soil around the enterprises in key areas of Hubei Province were monitored. The monitoring data were used to establish a 3layer BP neural network model with 13 inputs,1 hidden layer and 6 outputs.The content of Mn,Co,V,Ag,Tl,Sb in the monitoring area were predicted. The Nemerow index based on the monitoring and prediction results of heavy metals was used to evaluate the pollution of the area studied. The results showed that there were different levels of exceeding standard of heavy metals in the area studied. The maximum overstandard range was 1.8156.1 times. The relative error between the prediction results of six heavy metals including Mn,Co,V,Ag,Tl and Sb and the actually tested results was ranged from 0.3% to 19.9%. Mn,V,Ag,Tl,and Sb were significantly correlated with the confidence of 99% (P＜0.01,n=11).Co was significantly correlated with confidence of 95% (P＜0.05,n=11). The BP neural network prediction model constructed had good accuracy. Based on the BP neural network model,the Nemerow pollution index not exceeding the warning limit took over a proportion of 77.3%,with the proportion of light pollution of 17.4% and the ratio of moderate to severe pollution of 4.0% each.