The artificial neural network method is applied to crack defect recognition of aluminum alloy workpieces to overcome the limitations of traditional manual recognition, thereby improving the accuracy of crack defect recognition. By designing and constructing a water immersion ultrasonic detection system, waveform data of ultrasonic detection defects are obtained, and feature extraction is performed on the collected defect waveform data to extract useful feature information, and the wavelet denoising processing is used to input a probabilistic neural network as a characteristic signal, and the network training is performed to realize intelligent recognition of different crack sizes. Experimental results show that the method can improve the accuracy and detection efficiency of crack defect size identification, and has a good application prospect.