As the main cottonproducing province in China, Xinjiang has widely applied filmcovering technology. In the process of cotton mechanical picking, a large amount of film is also collected along with the seed cotton. If the film could not be thoroughly separated, it would be subsequently transformed into the ginned cotton together, which would reduce the quality of the textile. However, it is difficult to identify the film by using traditional methods, because the film is colorless and transparent without fluorescent effect. In order to detect the film covering the seed cotton, a novel algorithm was proposed based on shortwave nearinfrared hyperspectral imaging and deep learning. Firstly, considering the advantage of multichannel and model complexity, the variablewise weighted autoencoder was developed to weight hyperspectral image channel and transform them into lowdimension feature. Comparing with selecting one or deleting some channels directly, VW-AE was used to achieve information that was more useful and less influence on the negative feature. Then, several variablewise weighted autoencoders were stacked layer by layer to form deep networks, and a twolayer neural network combined with the BP algorithm was used to update the deep network weights. Next, the highlevel features from the deep network were set as the inputs of an extreme learning machine (ELM) whose parameters were determined by a particle swarm optimization method. Finally, the classification results of the ELM were merged into film and nonfilm two classes by morphology and connected domain technologies. Simulation experiments and a field test were carried out to evaluate the performance of the proposed algorithm. The results showed that the recognition rate of the presented algorithm was up to 95.5% and the separating rate of the film was 95%, which met the actual production requirements. 