Land evaluation factors often contain continuous-,discrete- and nominal-valued attributes. In traditional land evalu- ation, these different attributes are usually graded into categorical indexes by land resource experts,and the evaluation results rely heavily on experts’ experiences.In order to overcome the shortcoming, e presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly.A fuzzy back propagation neural network (BPNN),a fuzzy radial basis function neural network (RBFNN),a fuzzy BPNN ensemble,and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province.The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the singIe fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble,respectively. By using the fuzzy neural network ensembles,the validity of land resource evaluation was improved and reliance on land evaluators’ experiences was considerably reduced. Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evalua- tion, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts’ experience. In order to overcome the shortcoming, e presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values ​​directly. A fuzzy back propagation neural network (BPNN), a fuzzy neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the singIe fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lowe r than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators’ experiences was poor reduced.