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The study explores the risks and benefits of investors in e-commerce financing under the background of “double carbon” to maximize investors’ interests and reduce investment losses. The Back Propagation Neural Network (BPNN) algorithm model of e-commerce enterprise financing based on the Capital Asset Pricing Model (CAPM) is mainly studied. First, according to the worldwide literature, the theoretical concept and principle of the CAPM are deeply studied and analyzed. Then, from the perspective of “double carbon,” with the financing risk characteristics of listed companies responding to the “double carbon” policy as samples, the CAPM model of e-commerce financing under the BPNN algorithm is established. Next, the BPNN is used to input the financing samples of e-commerce enterprises and train the model. The verification experiment of the capital asset financing model of e-commerce enterprises is further conducted. The experimental results show that the model error is the smallest when the number of neurons in the hidden layer reaches about 20. Therefore, the number of neurons in the hidden layer of the model is set to 20. When the number of iterations in training reaches 3000, the financing risk model begins to show a convergence trend. Finally, it can be determined that the number of adaptive iterations of the model is 3000. When the learning rate is 0.03, the oscillation of the model is smaller and stabler, so the model learning rate is 0.03, and the final model error is only 9.96 × 10 − 8 . Based on this, e-commerce enterprises can achieve the purpose using this model to adjust the coefficient in financing in the future. The results have certain reference significance for e-commerce financing risk assessment under a “double carbon” background.

Chinese heating method has gradually shifted from small fragments to centralized regional heating. This heating method has achieved obvious energy-saving benefits. The article establishes an economic model of the energy utilization rate of the central heating system and installs the system exergy balance equation at the same time. The simulation found that the energy utilization coefficient of the heating system is high and has a high energy-saving potential. The effect of energy utilization is poor, and it has great potential for energy saving.