Digital inclusive finance is key to China’s agriculture and low-carbon economics. The panel data for China’s 30 provinces were chosen from 2011 to 2019. An SBM GML model was applied in the thesis to measure agricultural green total factor productivity (GTFP), and to determine how Digital Inclusive Finance would affect agricultural GTFP a two-way fixed effect model was created. This study found that, from 2011 to 2019, the advancement of Digital Inclusive Finance could effectively enhance and drive the continuous increase of agricultural GTFP in China. Specifically, agricultural GTFP is increased by 0.288% as a result of every 1% rise in the Digital Inclusive Finance index; Digital Inclusive Finance helps agricultural green technologies advance and become more effective. According to a mechanism test, Digital Inclusive Finance increases agricultural GTFP growth by improving green technology innovation. Further analysis shows that the development of agricultural GTFP is significantly related to the depth and digitalization of Digital Inclusive Finance, but not in terms of its breadth. The above findings provide new ideas and empirical evidence for revealing the connection among Digital Inclusive Finance and agricultural GTFP and, on this basis, designing and improving relevant policies.

The high-complexity, high-reward, and high-risk characteristics of financial markets make them an important and interesting study area. Elliott’s wave theory describes the changing models of financial markets categorically in terms of wave models and is an advanced feature representation of financial time series. Meanwhile, deep learning is a breakthrough technique for nonlinear intelligent models, which aims to discover advanced feature representations of data and thus obtain the intrinsic laws underlying the data. This study proposes an innovative combination of these two concepts to create a deep learning + Elliott wave principle (DL-EWP) model. This model achieves the prediction of future market movements by extracting and classifying Elliott wave models from financial time series. The model’s effectiveness is empirically validated by running it on financial data from three major markets and comparing the results with those of the SAE, MLP, BP network, PCA-BP, and SVD-BP models. Interestingly, the DL-EWP model based on deep confidence networks outperforms other models in terms of stability, convergence speed, and accuracy and has a higher forecasting performance. Thus, the DL-EWP model can improve the accuracy of financial forecasting models that incorporate Elliott’s wave theory.