<p>In machine learning, models are typically constructed by learning patterns from user-provided data. When users request the deletion of their data, it is necessary to remove this data’s influence and contributions from the model. However, current research rarely considers the issue from the user’s perspective. As a result, it has not fully explored whether the requested data for deletion truly needs to be removed entirely. In this paper, a novel data description and unlearning framework called Specific Synaptic Dampening over Data Description (SSDD) is proposed. From the perspective of user requests, data description is used to identify which requested data do not require forgetting, and this analysis is further extended to the entire dataset. Next, based on the data description, the correlation between the data and the model is defined. Finally, an adaptive neuron constraint algorithm is designed based on this information to improve forgetting efficiency and model performance. Theoretical analysis and experiments on representative datasets demonstrate the effectiveness and efficiency of SSDD, confirming that not all requested data need to be forgotten in reality.</p>