Abstract: OBJECTIVE To investigate the distribution characteristics of the pathogen spectrum and risk factors for mixed infections in neonatal pneumonia based on the metagenomic next-generation sequencing (mNGS). METHODS We retrospectively analyzed the clinical data of 412 neonatal pneumonia cases admitted to the Second Affiliated Hospital of Wenzhou Medical University from Mar. 2020 to Mar. 2025. All neonates underwent mNGS testing of respiratory specimens, with results compared to traditional pathogen detection. General information, perinatal factors, clinical characteristics and laboratory indicators were collected to analyze pathogen spectrum distribution. Neonates were divided into single-infection and mixed-infection groups based on infection status. Differences in clinical characteristics were compared, and univariate and multivariate logistic regression analysis were performed to identify risk factors for mixed infections. RESULTS Among the 412 cases, mNGS detected 303 (73.54%) single infections and 109 (26.46%) mixed infections, including 71 (17.23%) viral-bacterial, 24 (5.83%) bacterial-bacterial and 14 (3.40%) viral-fungal co-infections. Traditional pathogen detection identified 92 (22.33%) mixed infections. Multivariate logistic regression analysis revealed prematurity, low birth weight, early-onset neonatal pneumonia and mechanical ventilation as significant risk factors for mixed infections (P<0.05). CONCLUSIONS The pathogen spectrum of neonatal pneumonia is diverse, with a high incidence of mixed infections, predominantly viral-bacterial co-infections. Prematurity, low birth weight, early-onset disease and mechanical ventilation significantly increase the risk of mixed infections in diagnosed cases. mNGS aids in early identification of high-risk patients, providing critical evidence for precise prevention and individualized anti-infective therapy.