Data technology has improved the quality of life and education for all people around the world. With the ongoing spread of such technologies in education, the ways in which information is presented and used have also become more and more elaborate. This paper proposes improvements for web-based foreign language learning and optimises the distribution of foreign language teaching in a computer network environment. Specifically, it puts forward the proposal to use instructional frameworks to help integrate computer systems in foreign language courses and thus address structural imbalances and cognitive challenges in online language education. By applying educational standards and environmental principles, a model ecosystem was built to promote research and application in digital ecology for teaching foreign languages online. According to the experiments, the adaptive GA-BPNN model achieved convergence in as few as six iterations during training, with a stable network error sum of 0.19 and a 76% improvement in the convergence rate. In addition, the mean squared error dropped by 79%, and thus the adaptive GA-BPNN model could reach a good global optimum more quickly.
