The analysis of seismic data quality is crucial for identifying issues with seismic stations. Key parameters include timing accuracy, completeness, and ambient noise levels. Variations in station noise levels and deviations from global noise models can impact event detection capabilities. Microseismic noise is characterized by power density spectra (PSD) and ambient noise probability density functions (PDF). Seismic network operators typically review data quality through visual inspection of PSDs, but this process is subjective and time-consuming. An automated assessment system could streamline quality control and provide more objective results. This study proposes a fuzzy rule-based expert interpretation system that mimics human reasoning and incorporates operator knowledge. By extracting features from PSDs and PDFs, a evaluation system was developed using fuzzy interpretability rules. Real seismic station data demonstrated the system's robustness and its potential to be integrated into routine seismic network operations.