ABSTRACT Freshness is a core quality indicator that determines the utilisation and commercial value of fish products. Traditional fish freshness detection methods are highly subjective and destructive, while existing neural network models suffer from low detection accuracy, unsatisfactory recall and confidence scores and limited generalisation ability. To address these limitations, this paper proposes a novel Dual‐Sensitive Convolutional Neural Network (S‐CNN), where the letter ‘S’ stands for Sensitive. The model simultaneously extracts and fuses discriminative features from fish eye and gill images, capturing subtle freshness differences through a dual‐sensitive feature extraction mechanism. In data preprocessing, all image pixels are normalised to the range [0, 1] to unify numerical scales, stabilise gradient descent and mitigate overfitting. The proposed S‐CNN is composed of seven convolutional blocks, each equipped with batch normalisation, L2 regularisation and a pooling layer; the pooling operation is omitted in the last block to avoid excessive dimensionality reduction. After the flatten layer, a Dropout regularisation module is adopted, and L2 regularisation is applied to all convolutional and fully connected layers. The network uses categorical crossentropy as the loss function. Experimental results demonstrate that the S‐CNN achieves a detection accuracy of 98.70% and an average confidence score of 99.18% on the fish freshness dataset, outperforming other comparative models. The results confirm that the fusion of fish eye and gill features can effectively evaluate fish freshness, providing a reliable method for nondestructive detection and quality assessment of fish products.