The maintenance and prediction of turbofan engine lifespan are critical to modern aviation, playing a key role in ensuring safety and minimizing operational costs. This study addresses the challenge of predicting the RUL of turbofan engines by proposing a novel hybrid model that integrates Parallel TCN and Bidirectional BiLSTM. Traditional methods often struggle to capture both local features and long-term dependencies simultaneously; the proposed model overcomes this limitation by using TCN to extract short-term local features and BiLSTM to capture bidirectional temporal dependencies. To further improve feature importance recognition, an enhanced SE attention mechanism is introduced, which dynamically adjusts feature weights to better highlight critical information. Experiments conducted on the FD001 and FD003 subsets of the C-MAPSS dataset demonstrated that the proposed model achieved RMSE values of 12.15 and 11.16, and Scores of 230.4 and 209.84, respectively, outperforming other approaches in terms of accuracy.