The increasing complexity of the international situation intensifies the changes of the economic environment. People’s demand for information represented by accounting earnings, such as judging the profitability and risk coefficient of the company, is becoming more and more urgent. This study puts forward the theory of predicting accounting earnings through accounting earnings factors in a nonlinear way and designs an accounting earnings forecasting model based on artificial intelligence. Integrating LSTM, seq2seq, and reinforcement learning and combining with self-attention like mechanism, a complex multifactor time series forecasting model is established, and reinforcement learning is used to stabilize the model to prevent overfitting, which puts forward a new solution to the multifactor time series forecasting problem of complex relationship. The experimental results and comparative analysis show the effectiveness of the enhanced recurrent neural network accounting earnings prediction model designed in this study.

Under the background of energy conservation and emission reduction and large-scale promotion of electric energy substitution, fully exploring the complementary potential of various energy systems and realizing the optimization of comprehensive energy utilization are the most critical development goals of the current energy system. The key to achieving this goal is the investment potential analysis of integrated energy projects. In order to effectively solve the problems of difficult scientific determination of evaluation index weight and low accuracy of evaluation results in the analysis of investment potential of integrated energy projects, an investment potential analysis model of integrated energy project based on deep learning neural network is designed. The design process of the integrated energy project is summarized. The RBF-BP neural network model is established to obtain the correlation between the factors of the evaluation unit, further analyze and process the training results, and calculate the weight of the evaluation index. The obtained weight is substituted into the TOPSIS comprehensive evaluation model for the investment potential analysis of integrated energy projects. According to the investment potential analysis results, the investment potential analysis value of energy performance contracting (EPC) mode is 0.9122, which is the best operation mode. The results show that the analysis results reflect the investment potential of integrated energy projects more objectively and scientifically.