The model input of the data-driven method in the effective evaluation process of battery state, although related to capacity, does not consider its information content and quality. Low-quality data input can cause a certain degree of prediction bias. To address this issue, this paper proposes a weighted neural network battery SOH prediction and RUL estimation model that takes into account the degree of health factor information. Based on the GA-BP neural network, this model identifies effective health feature data sets and uses data information to generate momentum factors to ensure neural network iteration convergence speed. And this paper filters out low information health feature prediction findings using the entropy weight concept and then uses the filtered prediction results as the input to the battery aging model to further achieve the RUL estimation. It is discovered through the publicly available battery aging datasets and experimental platforms that the model's SOH prediction results have a MAE and RMSE range controlled within 0.63% and 0.81%, and the remaining useful life estimation results have a MAE and RMSE range controlled within 0.0031 mA·h and 0.0042 mA·h, indicating good feasibility and effectiveness.