Time slicing methods in dynamic networks greatly influence the accuracy of community evolution analysis results. As communities vary nonlinearly with time and network topology， both the existing uniform time slicing method and network topology variance-based nonuniform time slicing method are unsatisfactory in capturing community evolution events. Therefore， a nonuniform time slicing method based on prediction of community variance was proposed， where the community variance is quantitatively described by the difference between the community modularity expected to be achieved by the updated network and the community modularity obtained by directly applying the community detection results of the network before changing. Firstly， the prediction model of community modularity was established on the basis of time series analysis. Secondly， with the established model， the expected community modularity of the updated network was predicted， and the prediction value of community variance was obtained. Finally， once the prediction value surpassed a previously set threshold， a new time slice was generated. Experimental results on two real network datasets show that compared with the traditional uniform time slicing method and the nonuniform time slicing method based on network topology variance， on the dynamic network dataset Arxiv HEP-PH， the proposed method identifies community disappearance events 1.10 days and 1.30 days earlier， respectively， and identifies the community forming events 8.34 days and 3.34 days earlier， respectively， and the total number of identified community shrinking and growing events increased by 10 and 1 respectively. On Sx?MathOverflow dataset， the proposed method identifies community disappearance events 3.30 days and 1.80 days earlier， and identifies the community forming events 6.41 days and 2.97 days earlier respectively， and the total number of identified community shrinking and growing events increased by 15 and 7， respectively.