There is a link between changes of climate and the degree of eutrophication of lakes. Eutrophication of lakes has a negative impact on human health, ecosystems, and socioeconomics. Based on statistical data and remote sensing data, coupling models of Morlet wavelet analysis and BP neural network in different time scales were used to analyze the eutrophication trends of Lake Xingyun in Yunnan from 1986 to 2011 and their multi-scale relationships with the climatic variables including monthly precipitation, monthly average temperature, monthly average wind speed, and monthly sunshine duration. The results show that the fluctuation period of climatic factors are important for affecting the monthly variation in the bloom intensity. The coupling models of Morlet wavelet analysis and BP neural network can effectively improve the accuracy of data fitting. Goodness of fit of the optimal coupling model is 0.605 which is higher than the BP neural network's goodness of fit (0.292). The optimal coupling model can analysis and describe eutrophication better than the BP neural network. The mean square error and the correlation coefficients of the optimal coupling model were better than those by the BP neural network. The monthly average temperature was the dominant climatic factor affecting the eutrophication of Lake Xingyun, followed by the monthly precipitation, monthly average wind speed and monthly sunshine duration in the optimal coupling model. In conclusion, we show that the coupling models of Morlet wavelet analysis and BP neural network, which has better adaptability to periodically changing sample data and higher prediction accuracy than the BP neural network, can provide reliable reference for the protection and eutrophication control of Lake Xingyun.