Based on the observed data of precipitation and air temperature in Northern Shaanxi during 1960-2020, the characteristics of precipitation and air temperature at multiple time scales in Northern Shaanxi were analyzed by using CEEMDAN (Adaptive Complete Set Empirical Model) and back propagation neural network time series model. At the same time, the cross-wavelet and wavelet coherence methods were used to explore the factors affecting climate change in Northern Shaanxi. The results show that there are certain rules of precipitation and temperature in the decadal, interannual, seasonal and monthly scales in Northern Shaanxi. The interdecadal fluctuations of precipitation and temperature were dominant, and the periods were about 12-23 years and 13-21.1 years, respectively. According to the analysis of trend term, in addition to the stable fluctuation of precipitation in Northern Shaanxi, the temperature showed a fluctuating upward trend. Arctic oscillation index, Pacific decadal oscillation index, Niño 3.4 Region sea surface temperatures index and relative number of sunspots all have a certain influence on the climate change in Northern Shaanxi.

We take Shaanxi Province as the research area, aiming at exploring the information channel or path of climate change risk perception of Shaanxi people. It is desirable for us to carry out information channel or path classification of climate change risk perception based on survey data involving 5493 people in Shaanxi Province. Firstly, we use a Back Propagation (BP) neural network method to fit the information path of climate change risk perception. Secondly, a decision tree model is adopted to classify information channels of climate change risk perception. The results show that 300 neurons are needed in the information channel of climate change risk perception of Shaanxi people. The first path which influences climate change risk perception of Shaanxi people is as follows: indirect perception-direct perception-indirect perception-conductive perception. The second path is indirect perception-conductive perception. The third path is as below: indirect perception-direct perception-conductive perception, which also impacts climate change risk perception. According to varying information channels or paths of climate change risk perception, the public can formulate different risk management strategies to improve the level of climate change risk perception.