Stick-slip vibrations in drilling reduce efficiency and increase tool failure risks. Current identification methods, relying on surface data or lab models, poor real-time adaptability. This study proposes a novel method combining wavelet decomposition (Morlet wavelet) with multi-parameter logical discrimination to detect vibrations. Four drilling efficiency indicators -bit aggressiveness, cutting depth, torque variation index (TVI), and weight on bit (WOB) variation index-are analyzed from surface data. Wavelet decomposition extracts high-frequency components to quantify vibration intensity, with adaptive decomposition levels (1-3) balancing sensitivity and computational efficiency. Field-calibrated thresholds classify minor and severe vibrations. Validation using downhole acceleration data shows torque signals detect minor vibrations effectively (87-94% accuracy), while bit aggressiveness excels in severe stages. The method reduces reliance on downhole instruments, improves timeliness, and enhances recognition stability by 30-45% over conventional approaches, offering a cost-effective solution for real-time monitoring and risk mitigation.