To solve the problem that it is difficult to mine the features of different registers in large-scale corpus and it needs a lot of professional knowledge and manpower, a method to mine the features of distinguishing different registers automatically was proposed. First, the register was expressed as words, parts-of-speech, punctuations, and their bigrams, syntactic structure as well as multiple combined features. Then, the combination model of attention mechanism and Multi-Layer Perceptron (MLP) (i.e. attention network) was used to classify the registers into novel, news and textbook. And, the important features that were able to help to distinguish the registers were automatically extracted in this process. Finally, through the further analysis of these features, the characteristics of different registers and some linguistic conclusions were obtained. Experimental results show that novel, news, and textbook have significant differences in words, topic words, word dependencies, parts-of-speech, punctuations and syntactic structures, which implies that there will naturally present some diversity in the use of words, parts-of-speech, punctuations, and syntactic structures due to the different communication objects, purposes, contents, and environments when people utilize language.

In order to avoid the problem of blind discretization of traditional classification "discretize first learn second", a new method of associative classification based on lazy thought was proposed. It discretized the new training dataset gotten by determining the K-nearest neighbors of test instance firstly, and then mined associative rules form the discrete dataset and built a classifier for predicting the class label of test instance. At last, the results of contrastive experiments with CBA (Classification Based on Associations), CMAR (Classification based on Multiple Class-Association Rules) and CPAR (Classification based on Predictive Association Rules) carried out on seven commonly used quantitative datasets of UCI show that the classification accuracy of the proposed method can be increased by 0.66% to 1.65%, and verify the feasibility of this method.