In the process of oil and natural gas exploration and development, density logging curves play a crucial role, providing essential evidence for identifying lithology, calculating reservoir parameters, and analyzing fluid properties. Due to factors such as instrument failure and wellbore enlargement, logging data for some well segments may become distorted or missing during the actual logging process. To address this issue, this paper proposes a density logging curve reconstruction model that integrates the multi-head self-attention mechanism (MSA) with temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU). This model uses the distance correlation coefficient to determine curves with a strong correlation to density as a model input parameter and incorporates stratigraphic lithology indicators as physical constraints to enhance the model’s reconstruction accuracy and stability. This method was applied to reconstruct density logging curves in the X depression area, compared with several traditional reconstruction methods, and verified through core calibration experiments. The results show that the reconstruction method proposed in this paper exhibits high accuracy and generalizability.