Abstract: With the continuous development of big data technology, high-dimensional datasets have been increasingly widely applied in various fields. These datasets typically feature high dimensionality and excessive redundancy, which tend to induce model overfitting and increase computational costs. Feature selection helps extract an informative subset from them, reducing dimensionality and enhancing model interpretability. Although deep neural networks have shown potential in feature extraction, their performance is vulnerable to noise and outliers. To address these challenges, a novel feature selection method based on the intuitionistic fuzzy deep autoencoder (IFDAE) is proposed. First, the method fuses membership and non-membership degree information and applies intuitionistic fuzzy weights to handle uncertainty, thereby suppressing the impact of noise and outliers. Then, it fully learns the highly non-linear latent representations in the deep autoencoder, uses weight transfer to migrate pre-trained knowledge into the target network, and imposes a regularizer on the connection weight matrix between the input layer and the first hidden layer via the structured sparse norm to obtain a structurally sparse weight matrix, thus deriving the weight of each feature. Finally, to verify the effectiveness of the proposed IFDAE method, experiments are conducted on 12 public datasets and 1 real-world schizophrenia dataset. The results demonstrate that the proposed method exhibits significant superiority in both reconstruction capability and classification performance.