To address the problem of the difficulty of monitoring and controlling enterprise emissions， a Vertical Federated Learning Enterprise Emission Prediction （VFL-EEP） model with integration of electricity data was proposed by considering the premise of secure data sharing and privacy protection. Firstly， within the framework of Vertical Federated Learning （VFL）， the logistic regression model was enhanced to allow the separation of data usage and model training without leaking the monitoring data of electricity and environmental protection enterprises. Then， the logistic regression algorithm was improved to incorporate with Paillier encryption technology for ensuring the security of model parameter transmission， thereby solving the issue of insecure communication among participants in VFL effectively. Finally， through experiments on simulated data， the pollution prediction results of the proposed model were compared with those of the centralized logistic regression model. The results show that the proposed model integrates electricity data under the premise of privacy security， and has the accuracy， recall， precision， and F1 value improved by 8.92%， 7.62%， 3.95%， and 11.86%， respectively， realizing the balance between privacy protection and model performance effectively.

To address the problems of poor effects， easily falling into suboptimal solutions， and inefficiency in neural network hyperparameter optimization， an Improved Real Coding Genetic Algorithm （IRCGA） based hyperparameter optimization algorithm for the neural network was proposed， which was named IRCGA-DNN （IRCGA for Deep Neural Network）. Firstly， a real-coded form was used to represent the values of hyperparameters， which made the search space of hyperparameters more flexible. Then， a hierarchical proportional selection operator was introduced to enhance the diversity of the solution set. Finally， improved single-point crossover and variational operators were designed to explore the hyperparameter space more thoroughly and improve the efficiency and quality of the optimization algorithm， respectively. Two simulation datasets were used to show IRCGA’s performance in damage effectiveness prediction and convergence efficiency. The experimental results on two datasets indicate that， compared to GA-DNN（Genetic Algorithm for Deep Neural Network）， the proposed algorithm reduces the convergence iterations by 8.7% and 13.6% individually， and the MSE （Mean Square Error） is not much different； compared to IGA-DNN（Improved Genetic Algorithm for Deep Neural Network）， IRCGA-DNN achieves reductions of 22.2% and 13.6% in convergence iterations respectively. Experimental results show that the proposed algorithm is better in both convergence speed and prediction performance， and is suitable for hyperparametric optimization of neural networks.