The invention discloses a fatigue detection method based on multi-source information fusion. Electroencephalogram signals, twinkling information and electrocardiosignals of a testee are synchronously collected by means of an electroencephalogram collecting device and an electrocardiogram collecting device respectively; electroencephalogram signal features including the relative energy of electroencephalogram rhythm waves alpha, beta, theta and delta, electro-oculogram information including twinkling frequency E and twinkling intensity F, and electroencephalogram features including heart rate values HR, LF and HF are extracted; by means of the logistic regression algorithm, the fatigue degrees are primarily divided into three classes, namely, the non-fatigue degree, the mild fatigue degree and the deep fatigue degree, and meanwhile features with large weights are screened according to logistic regression weights for feature fusion; fused feature vectors are classified again by means of the bagging algorithm based on a support vector machine, the processed feature vectors serve as input of the bagging algorithm, and the current fatigue degree of the testee is determined; different fatigue relieving methods are used according to classification results of the fatigue degree of the testee. The method has the advantages of being high in applicability, high in fatigue detection precision, good in improvement effect and the like.