The invention discloses a non-linear time-varying process fault monitoring method based on high efficiency recursion kernel principal component analysis and belongs to the fault detection and diagnosis technology field. The method comprises steps that data having non-linear and slow time-varying characteristics and containing faults is acquired from a Tennessee Eastman process simulator, a Gauss kernel function is utilized to project the acquired normal data to the high-dimensional characteristic space and is centralized, an initial offline monitoring model is established, and a kernel densityestimation function is employed to determine control limit; secondly, when new process data is acquired, through introducing a first-order interference theory method, a model is directly updated based on a characteristic value and a characteristic vector acquired in the offline model, the new data is projected to the updated kernel space and the residual error space to calculate T2 and SPE statistics; when the corresponding control limit is surpassed, occurrence of a monitoring fault is determined, otherwise, the whole process operates normally. The method is advantaged in that two problems are mainly solved, 1), a problem of relatively high false alarm rate generated during fault monitoring in the non-linear time-varying process of kernel principal component analysis is solved; and 2), aproblem of relatively high load existing in a recursion algorithm based on characteristic constant decomposition is solved.