Abstract: OBJECTIVE To observe the efficiency of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in differential identification of source tracking of microbial contamination in the hospital and pharmaceutical manufacturing environments so as to provide evidence for the infection outbreak survey and quality control. METHODS Totally 90 microbial isolates were collected from 6 clean environments and were parallelly identified by using MALDI-TOF MS and 16S rRNA/ITS gene sequencing. The sequencing result was set as the golden standard, the phylogenetic tree was constructed through BLAST and RAxML, the cluster analysis of protein profile was performed with R language vegan package. The consistency and deviation types of identification on the levels of phylum, genus and species were systematically compared between the two methods. RESULTS All of the 90 isolates (87 strains of bacteria, 3 strains of fungi) were successfully identified by sequencing, covering 48 species across 5 phyla. MALDI-TOF MS yielded an overall identification rate of 57.78% (52/90), with 100.00% of the accurate rate at the genus level, 73.08% at the species level, but it failed to identify any fungi. Firmicutes showed the highest identification rate, followed by Actinobacteria and Proteobacteria. Misidentifications or clustering bias occurred among closely related species (Staphylococcus taiwanensis misidentified as S. haemolyticus) and heterogeneous strains (Micrococcus luteus), with overlapping peaks observed in the protein spectra within the 4800 to 5 500 m/z range. CONCLUSIONS MALDI-TOF MS enables highly precise, minute-scale rapid identification at the genus level, making it suitable for emergent screening of contamination sources during hospital infection outbreaks. However, the accuracy of identification on the species level is limited by database coverage and protein expression heterogeneity. It is recommended to integrate 16S rRNA/ITS sequencing to establish localized databases, thereby enhancing the reliability and timeliness of microbial source tracking in clinical and industrial settings.