When predicting the emotional polarity of a specific aspect， there is a problem of only depending on a single aspect item and ignoring the emotional dependence between aspect items in the same sentence， a Multi-layer Multi-hop Memory network with Aspect Item and Context Representation （AICR-M³net） was proposed. Firstly， the position weighting information was fused by Bi-directional Gated Recurrent Unit （Bi-GRU）， and the hidden layer output was used as the input of the mixed context coding layer to obtain a context representation with higher semantic relevance to the context. Then， Multi-layer Multi-hop Memory Networks （M³net） was introduced to match aspect words and context many times and word by word to generate aspect word vectors of specific context. At the same time， the emotional dependence between specific aspect item and other aspect items in the sentence was modeled to guide the generation of context vector of specific aspect item. Experimental results on Restaurant， Laptop and Twitter datasets show that the proposed model has the classification accuracy improved by 1.34， 3.05 and 2.02 percentage points respectively， and the F1 score increased by 3.90， 3.78 and 2.94 percentage points respectively， compared with AOA-MultiACIA （Attention-Over-Attention Multi-layer Aspect-Context Interactive Attention）. The above verifies that the proposed model can deal with the mixed information with multiple aspects in context more effectively， and has certain advantages in dealing with the sentiment classification task in specific aspects.

For the vacancies on digital watermarking technology based on 2D-vector animation, this paper proposed a blind watermarking scheme which made full use of vector characteristics and the timing characteristics. This scheme adopted color values of adjacent frames in vector animation changed elements as embedded target. And it used Least Significant Bit(LSB) algorithm as embedding/extraction algorithm, which embedded multiple group watermarks to vector animation. Finally the accurate watermark could be obtained by verifying the extracted multiple group watermarks. Theoretical analysis and experimental results show that this scheme is not only easy to implement and well in robustness, but also can realize tamper-proofing. What's more, the vector animation can be played in real-time during the watermark embedding and extraction.

With serious sidelight, it is difficult for the traditional algorithm to eliminate shadows. To improve the illumination compensation effect, a logarithmic transformation function was presented. In order to improve the performance of face recognition, by taking this problem as a classic pattern classification problem, a new method combining Local Binary Pattern (LBP) and Support Vector Machine (SVM) was proposed. One-against-one was used to convert multi-class problem to two-class problem, that can be used by SVM. Simulation experiments were conducted on the database of CMU PIE, AR, CAS-PEAL and one face database collected by the authors. The results show that lighting effects can be well eliminated and the proposed method performs better than the traditional ones.