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Based on the fact that one can attain knowledge by oneself, which is different from epidemic spreading, we analyze the knowledge transmission in complex networks. In this paper, we propose a knowledge transmission model by considering the self-learning mechanism and derive the mean-field equations that describe the dynamics of the knowledge transmission process. Furthermore, we obtain the transmission threshold R 0 , which is closely related with the transmission rate and self-learning rate. Moreover, we investigate the global stability of the knowledge free equilibrium E 0 and the endemic equilibrium E * of the model. That is, when R 0 < 1, the knowledge free equilibrium point E 0 is globally asymptotically stable and the knowledge becomes completely extinct eventually; when R 0 > 1, a unique endemic equilibrium point E * is globally stable, and the knowledge can be transmitted. Finally, numerical simulations are given to illustrate the theoretical results. The simulation results indicate that the self-learning factor has an obvious promoting effect on the knowledge transmission, both in scale-free and homogeneous networks. Besides, the simulation results illustrate that the scale-free network is more efficient to knowledge transmission. [ABSTRACT FROM AUTHOR]