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Mobile learning is a kind of learning mode by using mobile devices, and it is an indispensable way of learning strategy in colleges and universities. The authors conducted the interviews and questionnaires about the teaching situation, learning strategies, using of network resources, and so on. Next, the authors checked and verified carefully the feedback data from classroom teaching. In the process of investigation, the students were divided into two groups. The authors analyzed the mean and standard deviation of the two groups of data tables. According to the data reliability analysis, exploratory factor analysis, significance analysis, the authors propose the teaching mode of “one heart, two sides and six links(OHTSSL)” based on mobile learning strategy. In order to construct new cognitive content and train students' innovation ability, teacher and students must implement the mobile learning strategy in classroom teaching. Teacher and students execute teaching process of six links based on OHTSSL teaching mode.

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In this paper, we exploit a method for identifying flaws on product surface based on spatial connectivity domain. A number of algorithms for detecting local features exist that were established to enhance the efficiency and accuracy of identifying interest features, such as AKAZE , BFSIFT , BRIEF , BRISK , ORB , SURF , SIFT and PCA-SIFT algorithm. But the data of flaws on product surface which is similar and consistent with the background intensity became a dilemma to detect the feature of image. In terms of identifying flaws on product surface, the above algorithms are not effective and accurate. Our aim is to enhance the accuracy of detecting the feature of flaws on product surface, so that the product with flaws could be accurately identified in industrial production. Therefore, we propose a method to identify flaws on product surface based on spatial connectivity domain. Compared with some other algorithms, the proposed method is more effective and accurate in detecting the local feature flaws on product surface of auto parts in automotive manufacturing factory.