Application of DL-based algorithms in green policy assessment and prediction of ecological changes

After experiencing a high-speed development stage driven by factors and investment, China has witnessed a rapid growth in its overall output. However, behind this remarkable growth, there are a series of severe issues such as resource scarcity, environmental pollution, and ecological damage. These problems not only pose a threat to China's sustainable development but also have far - reaching impacts on the global ecological environment. Therefore, promoting the green and low - carbon transformation has become an important way for China to achieve its "dual carbon" goals and high - quality development. This article focuses on the evaluation and optimization of data - driven green policies. By means of data analysis, prediction, and evaluation, it aims to offer a scientific basis for the formulation and improvement of green policies. To reach this objective, an ecological change prediction algorithm based on deep learning (DL) is proposed. This algorithm can make full use of historical ecological data, explore potential patterns and trends within the data, and accurately forecast future ecological changes, providing a scientific foundation for policy - making. The experimental results demonstrate that the algorithm has high accuracy and reliability in predicting ecological changes.