The size of the dataset is one of the key factors affecting the performance of deep learning models. Since the performance of deep learning models is highly dependent on the size of the dataset, the amount of data required to achieve a specific accuracy is usually difficult to estimate. This problem also exists in the intelligent design of metamaterials, and has become an important factor restricting the accuracy and efficiency of modeling. To this end, a dynamic data generation and model performance evaluation framework is proposed to achieve dynamic monitoring of the size of the dataset and model performance. In order to improve the efficiency of dynamic evaluation of the model and effectively alleviate the catastrophic forgetting phenomenon, a continuous learning strategy is designed so that the model only needs to learn new data during the dynamic evaluation process while maintaining the memory of existing knowledge. Experimental results show that the average prediction accuracy of the model trained based on this continuous learning strategy can reach 93.28%, and the average forgetting rate is 3.68%, which fully verifies the effectiveness of the model in alleviating the problem of catastrophic forgetting.