Eddy current imaging detection of titanium plate is susceptible to noise from industrial field. It is difficult to extract effective features from the detected images containing noise, which affects the classification accuracy. To address this issue, a classification method for eddy current detection images of titanium plate defects based on stacked sparse denoising autoencoder (SSDAE) deep neural network is proposed. Sparsity constraint is introduced into the denoising autoencoders (DAE) and the autoencoders perform unsupervised selflearning layerbylayer. Then, the autoencoders are stacked and a logistic regression (LR) layer is added to construct the deep neural network. The deep neural network can automatically extract features and classify eddy current detection images of titanium plate defects after supervised finetuning. The feature learning ability is improved by sparsity constraint, and the robustness of deep network is improved by stack combination of denoising autoencoders. Experimental results show that, compared with other traditional methods, the proposed method not only has higher classification accuracy in ideal conditions, but also can resist noise and classify defects of titanium plate in complex conditions more effectively.