For walnut quality classification problem in Xinjiang, the author selects feature parameters data, and research classification of the training and testing work using BP network of steepest descent algorithm of neural network, the self-organizing competitive algorithm based classification model and probabilistic neural network algorithm. The experimental results show that momentum BP network algorithm is relatively simple and intuitive in the implementation, meanwhile the network convergence speed is slow compared with three kinds of algorithm. Under the appropriate selection of the momentum factor, error convergence can achieve a minimum turbulence in a certain range; self-organizing competitive network can achieve better results about detailed classification of clustering in classification problems of dense distribution samples within the scope of the predefined categories; probabilistic neural network has better network convergence speed. Experimental results may provide certain theoretical basis to achieve the automation of walnut kinds of nuts and improve the work efficiency.
