In the inspection of spiral-welded pipelines, conventional methods often struggle to balance the extraction of temporal and spatial features while maintaining efficient model parameter optimization. To address these challenges, this study proposes a dynamic composite optimization detection model based on deep learning. Ultrasonic guided wave signals are acquired through sensors, where spatial features are extracted using a convolutional neural network and temporal dependencies are modeled via a long short-term memory network. To enhance model robustness, the whale optimization algorithm is employed to optimize four critical hyperparameters: the number of CNN filters, LSTM units, learning rate and Dropout rate. Comparative experiments were conducted on high-noise, low-noise and normal datasets. The results show that the accuracy rates of the proposed detection model have reached 98.88%, 99.7% and 100% respectively, and the average absolute errors have decreased to 0.195 5, 0.177 and 0.095 respectively. It verifies the detection performance advantages in the complex environment of high noise and multiple interference, and provides a theoretical basis for the spiral weld pipeline detection based on ultrasonic.