The number of bamboo stem at different ages and the mean diameter at breast height(DBH)which are the important target in evaluating productivity of bamboo stand were investigated in 50 plots established in Jianou city, Fujian Province in this paper, and the authors selected the method of artificial neural network to biuld the simulative and predictive model of mean DBH for bamboo stands. Artificial neural network is a good method in handling the overall nonlinear mapping problems between input variables and output ones, which has a wide application in many research fields, such as system simulating, automation controlling, paralleled data processing and so on. In this paper, the input variables were the number of different age and the total number of stand, the output variable was mean DBH for bamboo stands, the number of neurons of hide level( M ) was M=2L+1=3 according to the last document ( L is the number of factors of input level), and the network activity function is Sigmiod function as follows: F(x)=1/(1+e -x ). Using the built BP network, the samples were trained until E j(W 1 lm ,W 2 mn )=Nn=1(O nj -Y nj ) 2 =min, where O nj and Y nj are output values of network and really values of DBH for bamboo stands respectively, N is the number of trained samples, and E j is sum of square deviation of BP network. If E j didn’t converge, the weights and thresholds of BP network were adjusted as follow: ΔW ij (n+1)=βλ jX i+αΔW ij (n) and Δη j(n+1)=-βλ j+αΔη j(n) .. The results showed that the mean simulative accuracy and the mean predictive accuracy of mean D.B.H BP model for bamboo stands were all satisfactory, which were 89 95% and 89 26% respectively. Therefore, it provided a scientific basis for evaluating the productivity and realizing high yield for bamboo stands. The number of bamboo stem at different ages and the mean diameter at breast height (DBH) which are the important target in the productivity productivity of bamboo stand were investigated in 50 plots established in Jianou city, Fujian Province in this paper, and the authors selected the method of artificial neural network to biuld the simulative and predictive model of mean DBH for bamboo stands. Artificial neural network is a good method in handling the overall nonlinear mapping problems between input variables and output ones, which has a wide application in many research fields, such as system simulating, automation controlling, paralleled data processing and so on. In this paper, the input variables were the number of different age and the total number of stand, the output variable was mean DBH for bamboo stands, the number of neurons of hide level (M) was M = 2L + 1 = 3 according to the last document (L is the number of factors of input level), and the network activity function is S Using the built-in BP network, the samples were trained until E j (W 1 lm, W 2 mn) = Nn = 1 (O nj = 1 / (1 + e-x) -Y nj) 2 = min, where O nj and Y nj are output values ​​of network and really values ​​of DBH for bamboo stands respectively, N is the number of trained samples, and E j is sum of square deviation of BP network. If E j did not converge, the weights and thresholds of the BP network were adjusted as follow: ΔW ij (n + 1) = βλ jX i + αΔW ij (n) and Δη j (n + 1) = - βλ j + j (n) .. The results showed that the mean simulative accuracy and the mean predictive accuracy of the mean DBH BP model for bamboo stands were all satisfactory, which were 89 95% and 89 26% respectively. Thus, it provided a scientific basis for evaluating the productivity and realizing high yield for bamboo stands.