As a new research task, suggestion mining has important application value. Since traditional suggestion sentence classification methods have problems like complex rules, large labeling workload, high feature dimension and data sparsity, a PU (Positive and Unlabeled)-based suggestion sentence classification method was proposed. Firstly, some suggestion sentences were selected from an unlabeled review set by using a simple rule to form a positive example set; then a reliable negative example set was constructed by Spy technique in the feature space of autoencoder neural network to reduce the feature dimension and alleviate data sparsity; finally, Multi-Layer Perceptron (MLP) was trained by the positive example set and the reliable negative example set to classify the remaining unlabeled samples. On a Chinese dataset, the F1 value and the accuracy of the proposed method, reached 81.98% and 82.67% respectively. The experimental results show that the proposed method can classify suggestion sentences effectively without manually labelling the data.

Concerning the deficiency of the digital image scrambling algorithm in double scrambling, an image scrambling algorithm based on cloud model was proposed. The algorithm used the function value generated by the three-dimensional cloud model to change the positions and values of the image pixels, to achieve a double scrambling. The experimental verification as well as quantitative and qualitative analysis show that the scrambling image renders white noise and realizes the image scrambling. There is no security issue on cyclical recovery. The algorithm can quickly achieve the desired effect, resistant to shear, plus noise, filtering and scaling attacks. This proves that the algorithm is effective and reasonable, and also can be better applied to the image scrambling.