In recent years, with the improvement of people’s living standards, the ecological and artistic aspects of landscape design have attracted much attention. The study selects nine wetland restoration art projects in Yangtze River Delta as case studies for analysis and research. Based on the data from the questionnaire survey, gray correlation analysis is used to determine the weights for predicting the synergistic innovation effect of ecological fine arts and landscape design. On this basis, a BP neural network prediction model of collaborative innovation between ecological fine arts and landscape design is constructed, and the feasibility of this paper’s method is verified through experimental tests. The results show that there are three high correlation factors for the synergistic innovation of ecological fine arts and landscape design, which are ecosystem stability, adaptive management and ecological function restoration. In the comparison experiments, the average absolute percentage error of the prediction results based on this paper’s model is 3.13% lower than that of the time series analysis method, which indicates that the prediction based on this paper’s model has better adaptability, real-time and accuracy, and it can be fused with a wide range of data, and the overall prediction performance is better than the traditional statistical methods.
