Abstract. Protein structure prediction is a main task in the field of bioinformatics, and the prediction of protein secondary structure is the key point of this task. Extracting representative features and efficient classification methods are the basis of the prediction process. In this paper, a prediction method based on BP neural network is proposed. We use valid protein features extracted by variable-sized sliding window and two different encoding modes (5-bit encoding and Profile encoding) as input data, to make predictions for the secondary structure of proteins. The prediction accuracies are calculated by Jackknife test on three commonly used low-similarity protein datasets: 25PDB, 1189 and 640, and this method achieves a high overall accuracy upon these three datasets.

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Nowadays, more and more people are getting engaged in the construction of the Internet, consciously or not, by posting their individual comments on it. In today's big data era, opinion mining on customer's opinions has become one of the most effective ways to roundly use the great amount of information. Opinion mining, a brand new section of unstructured information mining, is mainly related to emotional analysis, features digging and subjective comments recognition and so on. It is also an important part of knowledge discovery, often used to extract hidden information from unstructured or semi-structured data. In the field of key algorithm for opinion mining and integrating, opinion integration algorithm means a calculating method, which ignores the non-significant internal parts of the comments. That is, skipping the minor issues from the users' comments, and focusing on the section of useful information, then summing up with some valuable conclusions for practical application. The research of opinion integration algorithm consists of four parts, namely, opinion spam detection opinion summarization, opinion visualization and opinion assessment. This paper focuses on opinion spam detection methods. Spam refers to fake user reviews, which means welldesigned fake comments targeted at enhancing or damaging a specific product by an individual or an organization. Therefore, identifying spam comments becomes an important task for improving the authenticity and accuracy of opinion mining. We regard this task a classification problem. With the use of wed crawlers, segmentation system and artificial labeling methods, we acquired a big amount of online comments. By training these data and selecting the relevant features, we finally build a classifier. The results from this experiment show that the methods provided herein can achieve the purpose of preliminary comment spam detection.