To evaluate reservoir porosity accurately, a method based on the bidirectional temporal convolutional network (BiTCN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM) optimized by the improved sparrow search algorithm (ISSA) is proposed. Firstly, the sparrow search algorithm improved by a phased control step size strategy and dynamic random Cauchy mutation is introduced. Secondly, the superiority of the ISSA is confirmed by the test functions of Congress on Evolutionary Computation in 2022 (CEC-2022). Furthermore, the experimental findings are assessed using the Wilcoxon test, which provides additional evidence of the ISSA’s superiority against the competing algorithms. Finally, the BiTCN-BiLSTM-AM is optimized by the ISSA, and the ISSA-BiTCN-BiLSTM-AM was applied to reservoir porosity construction in the Midlands basin. The results showed that the RMSE and MAE of the proposed model were 0.4293 and 0.5696, respectively, which verified the effectiveness and success rate of reservoir parameter construction by addressing the shortcomings in the capabilities shown by conventional interpretation procedures.