Sleep apnea affects quality of life and health seriously. PolySomnoGraphy （PSG） is the “gold standard” for diagnosis of sleep apnea， but it is expensive and inconvenient for long-term monitoring. Based on the above， a new method based on universal smart wristband was proposed to detect sleep apnea conveniently. In the method， by analyzing heart rate， blood oxygen saturation， and sleep state data collected by the wristband， an adaptive physiological data reconstruction method and a data interpolation method were used to achieve noise filtering； in feature engineering， continuous physiological variables and categorical variables were fused to extract sleep state features deeply； in the classification module， a lightweight Gated Recurrent Unit （GRU） model was used to simplify the training process and reduce the risk of overfitting. Experimental results show that the proposed method obtains 93.68% accuracy and 93.97% recall on a 23-person dataset. Correlation analysis shows that blood oxygen saturation， body mass index， and age are confirmed as key features for sleep apnea detection. Compared with PSG， the proposed method is more suitable for long-term monitoring in a home environment.