Method for quantifying centrality and difficulty of knowledge units

The present invention discloses a method for quantifying centrality and difficulty of knowledge units. The method comprises: in the centrality of the knowledge units: using a local centrality method to calculate the local importance degree of the knowledge units; using the local importance degree to calculate the learning migration probability matrix P between the knowledge units; by combining thecharacteristics of the knowledge units and the absorbed Markov chain, dividing the knowledge units into a transition state and an absorption state; and calculating the average transfer order matrix Qof the knowledge unit in the transition state i which is transferred to the transition state j before being absorbed by the knowledge unit in the absorption state, and a probability matrix M of the transition state which is finally absorbed by the absorption state, so as to obtain a calculation formula of the centrality. The method further comprises: in the difficulty measurement of the knowledgeunits, measuring the level