Pneumonia, a common respiratory infection worldwide, often leads to various complications, making its precise classification a critical issue in clinical diagnosis and treatment. This study addresses the need for accurate classification of respiratory infections and pneumonia by developing an effective diagnostic method based on the Raman spectroscopy of respiratory mucus. Initially, respiratory mucus samples from normal individuals, patients with common pneumonia, and those with concomitant plastic bronchitis were collected. Through Raman spectroscopic analysis, molecular features and chemical changes related to mucin glycosylation and fibrosis in each group were accurately identified, detailing the components and molecular bond alterations associated with the disease. Subsequently, combining principal component analysis and partial least squares discriminant analysis, a classification model capable of distinguishing between different types of pneumonia was constructed. Experimental results demonstrated high accuracy of the model in classifying pneumonia, with an overall classification accuracy reaching 99.08%, and specifically, 100% and 97.4% accuracy in distinguishing common pneumonia and plastic bronchitis, respectively. The study not only confirms the potential of Raman spectroscopy in the diagnosis of infectious diseases but also provides a reference for the broader application of molecular spectroscopic techniques in infectious disease diagnostics.