Abstract: Abstract In order to improve the defect recognition effect of small-diameter pipe elbows, a Bayesian optimized BiLSTM defect recognition method was proposed. A three-dimensional magnetic leakage simulation finite element model of ϕ114×8 mm (wall thickness) pipe elbow was established, data signals of different types of defects were obtained, multimodal features of signal waveforms were extracted as a library of feature samples, and the key hyperparameters of BiLSTM were adjusted by using Bayesian optimization, and the recognition effect of the method was verified by experiments. The results showed that the Bayesian optimized BiLSTM can identify the defect types of small-diameter pipe elbows more accurately, and the accuracy of the improved model reached 96.07%. The method was highly feasible for the identification of magnetic leakage defects in small-diameter pipe elbows.