Extensive investigations of X-ray films of different lung diseases will help to precisely distinguish and predict various diseases. Herein, an algorithm for chest X-ray disease classification based on an efficient channel attention mechanism is proposed. The high-efficiency channel attention module is added to the basic feature extraction network in a densely connected manner to improve the transmission of effective information in the feature channel while inhibiting the transmission of invalid information. By using asymmetric convolution blocks to improve the ability of network feature extraction, the multilabel loss function is used to address multilabeling and data imbalance. The novel coronavirus pneumonia X-ray film is added to the public dataset, Chest X-ray 14, to form the dataset, Chest X-ray 15. The experimental results on this dataset show that the average area under curve (AUC) value of the proposed chest X-ray-film disease classification algorithm based on the efficient channel attention mechanism reaches 0.8245, and the AUC value for pneumothorax reaches 0.8829. Thus, the proposed algorithm is superior to comparison algorithms.