Belief Peaks Clustering （BPC） algorithm is a new variant of Density Peaks Clustering （DPC） algorithm based on fuzzy perspective. It uses fuzzy mathematics to describe the distribution characteristics and correlation of data. However， BPC algorithm mainly relies on the information of local data points in the calculation of belief values， instead of investigating the distribution and structure of the whole dataset. Moreover， the robustness of the original allocation strategy is weak. To solve these problems， a fuzzy Clustering algorithm based on Belief Subcluster Cutting （BSCC） was proposed by combining belief peaks and spectral method. Firstly， the dataset was divided into many high-purity subclusters by local belief information. Then， the subcluster was regarded as a new sample， and the spectral method was used for cutting graph clustering through the similarity relationship between clusters， thus coupling local information and global information. Finally， the points in the subcluster were assigned to the class cluster where the subcluster was located to complete the final clustering. Compared with BPC algorithm， BSCC has obvious advantages on datasets with multiple subclusters， and it has the ACCuracy （ACC） improvement of 16.38 and 21.35 percentage points on americanflag dataset and Car dataset， respectively. Clustering experimental results on synthetic datasets and real datasets show that BSCC outperforms BPC and the other seven clustering algorithms on the three evaluation indicators of Adjusted Rand Index （ARI）， Normalized Mutual Information （NMI） and ACC.

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.