As a large agricultural nation, China is faced with low efficiency of agricultural land use and sharp reduction of the quantity of agricultural land. It is urgent to alleviate the relationship between population and land, and improve the efficiency of agricultural land intensive use when China embarks on solving problems of agricultural land. Therefore, it reveals great significance to evaluate the intensive use of agricultural land. Taking Shaanxi Province as an example, the dynamic fuzzy neural network is applied to evaluate the intensive use of agricultural land in order to overcome the low learning process and rule disasters existed in traditional methods. Aiming to improve the accuracy of evaluation, combination of qualitative and quantitative analysis is used to select evaluation index system without high level of redundancy through eliminating cropland balancing index and labor force per hm² quantitatively and the model receives fine convergence without exceeding 3×10^-16 in errors. In order to analyze evaluation results, evaluation scores obtained from the new model are clustered into four classes using the K-means method. Compared with the actual situation of agricultural land intensive using, the results reveal that intensive degrees of agricultural land distribute with spatial division in accordance with actual situations in Shaanxi and Yangling is the highest in intensive degrees of agricultural land. Finally, intensive degrees of agricultural land and per capita net income of farmers have positive significance correlation through stepwise regression analysis. The correlation coefficient is 0.74, and is higher than intensive degrees and GDP per capita and urbanization degrees, coming to a conclusion that per capita net income of farmers is the primary factor of affecting intensive degrees of agricultural land.