This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is used to retain more semantic information to generate features. The model was applied to species in Southern California, USA, citing SWOT analysis data to train the model. Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them, effectively solving the problem of insufficient data collection in development environments. The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) classification assessment commonly used in the environmental data domain. This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes in power load data, subsets of different categories of data were used for BiGRU training based on attention mechanism, and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information. To further optimize the parameter configuration of the meta model, Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters. Multiple sets of comparative experiments were designed, and the results showed that the average absolute error of the method in this paper was reduced by about 8.33% and 4.28%, respectively, compared with the single model and the combined model, and the determination coefficient reached the highest of 95.99, which proved that the proposed method has a better prediction effect.