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With the development of Internet finance, P2P lending (Peer-to-Peer Lending) have been popularized rapidly. However, due to the lack of a perfect credit risk identification system, the extremely high rate of bad debts has caused huge loss to operators and investors of P2P platforms. Aiming at the problem of high latitude and large scale of network loan data and low accuracy of single classifier’s default identification. The Lending data of the fourth quarter of 2018 on Lending Club platform in the United States was taken as the research object. Firstly, IV information value and Spearman-Boruta algorithm were combined to extract features, and a credit evaluation index system was established based on 5C credit evaluation principle widely recognized in the financial sector. Then, super parameter tuning was performed by the method combining Random search and 5 fold crossover validation. Finally, Stacking algorithm is adopted to integrate four base classifiers, Namely ANN (Artificial neural networks), RF (Random forest), AdaBoost (Adaptive Boosting) and XGBoost (Gradient boosting), to realize the default discrimination of network loans. The results show that the Stacking had better performance overall than the single classifier. Compared with the best performance XGBoost in the base model, the Accuracy, Precision, Recall, F1-score and AUC of Stacking model are improved by 0.41%, 0.45%, 0.28%, 0.37% and 0.42%, respectively. The Stacking model based on the fusion of multiple models can effectively reduce the error rate of misjudging defaulting customers as non-defaulting customers, and it is a default identification model that can meet the demand of lending business better, providing a scientific basis for the credit default prediction in financial sector.

Energy management is the key to China’s economy prosperity and environment protection in the future. This paper makes predictions on whether China’s energy 13th Five-Year plan can be achieved and the trend of energy consumption in the next stage. Aiming at the shortcoming of insufficient extraction of nonlinear features in the energy system from existing energy consumption models, and low prediction accuracy due to single models. Based on the derivation of the joint posterior distribution density function of the stationary ARMA model parameters under the Normal-gamma prior distribution, this paper proposes a Bayesian ARIMA-nonlinear regression energy consumption prediction model. Bayesian parameter estimation makes full use of prior information and Markov Chain Monte Carlo (MCMC) technique gets rid of the tedious calculation of high-dimensional integrals. Compared with the existing model, the improved model has higher prediction accuracy. Its Root Mean Square Error and Mean Absolute Percentage Error are 3452.0294 and 0.4727%. In line with the development goals set out in the 13th Five-Year plan, an empirical study of the fluctuation law of China’s total energy consumption was carried out using data on China’s energy consumption from 1990 to 2019 and its factors. The results show that affected by the Corona Virus Disease 2019 epidemic, the growth rate of China’s energy demand will fluctuate slightly in 2020. By the end of the 13th Five-Year plan, the total energy consumption can reach the expected goal. After the epidemic is under control, energy demand for the whole society will usher in a rebound growth. In the next five years (2020∼2024), China’s energy consumption will continue to maintain a steady upward trend of slowing growth. In view of these situations, this paper suggests that, on the one hand, we should attach equal importance to energy conservation and technology development to improve energy efficiency; on the other hand, we should adjust industrial structure and establish a unified energy management system to promote sustainable development of the energy industry.