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Salt stress can lead to a decrease in cotton fiber quality and yield, especially during the seedling stage when it is most affected by salt stress. In order to achieve rapid diagnosis of salt stress in cotton seedlings, rapid chlorophyll fluorescence technology was used to obtain OJIP curves of cotton seedling canopy leaves under different degrees of salt stress, and deep residual network (ResNet) and dilated convolution structures were combined to construct a 1D-deep residual dilated convolutional neural network (1D-DRDC-Net) cotton seedling salt stress deep learning diagnosis model-based on “l(fā)eaf-position channel” fluorescence data fusion. The results showed that salt stress significantly led to a decrease in water content in cotton seedlings, an increase in malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, and peroxidase (POD) activity. The trend of salt stress on cotton seedlings in the vertical direction showed significant changes in various parameters of the upper leaves of the plant, with L1 being the most sensitive leaf position to stress, while mature leaves were relatively less affected. Compared with other models, the diagnostic accuracy of 1D-DRDC-Net for three salt concentration gradients (0 mmol/L, 100 mmol/L and 200 mmol/L) under different stress times in cotton seedlings was 76.67%, with an F1-score of 76.48%, which was 5 percentage points higher than the accuracy of support vector machine (SVM) and back propagation neural network (BPNN), 14.45 percentage points higher than that of random forest (RF), and 3.34 percentage points higher than that of bidirectional long short-term memory (Bi-LSTM). The fluorescence information fusion strategy-based on “l(fā)eaf-position channel” was more effective than using only a single sensitive leaf position fluorescence information by 8.89 percentage points. Its robustness and generalization ability were stronger than that of models that only use ordinary convolution kernels and cancel “skip connections”. Finally, the established 1D-DRDC-Net model achieved diagnostic accuracies of 83.33%, 88.33%, and 95.00% on the 7th, 14th, and 21st day after cotton seedlings were subjected to salt stress, respectively. The research results can provide theoretical basis for cotton cultivation management.