With the explosive growth of data volume, edge computing plays an increasingly important role in big data processing. In general, the data generated by real applications is modelled and represented as high-order incremental tensors. Recently, the incremental tensor tucker decomposition is deemed as an efficient approach to extract the information inherent in those high-order massive data. As the traditional incremental tensor decomposition usually ignores the influence of tensor model characteristics on the decomposition process, it is rather difficult for the decomposition results to preserve the overall characteristics of the original data. To address these issues, we propose an incremental tensor tucker decomposition method ITTDMC(incremental tensor tucker decomposition based on mode characteristics) based on mode characteristics. First, the update order of the increment factor matrix is determined by the increase of mode length, for reducing the reconstruction error caused by the update order. Next, the update weight of the incremental factor matrix is computed according to the changing ratio of the mode entropy, such that the decomposition results enable to maintain the characteristics of each module more accurately. Furthermore, the previous model characteristics and update parameters are recorded in a guide tensor. When the incremental data with similar model characteristics needs to be processed, the corresponding update parameters of the guide tensor can be directly employed to reduce the computational costs. Extensive experiment results on the synthetic and real data sets exhibit that the ITTDMC can greatly reduce the reconstruction error of incremental tensor(up to 29%) for those data sets with strong model characteristics.