With the continuous development of remote sensing technology， there has been a significant increase in the volume of remote sensing data. Providing accurate and timely remote sensing information recommendation services has become an urgent problem to solve. Existing remote sensing image recommendation algorithms mainly focus on user portrait， overlooking the influence of image content semantics on recommendation results. To address these issues， a remote sensing image recommendation method based on content interpretation was proposed. Firstly， an object extraction module based on YOLOv3 was used to extract objects from remote sensing images. Then， the location distribution vectors of key objects were integrated as image content information. Additionally， a multi-element user interest portrait was constructed and dynamically adjusted based on the user’s active search history to enhance the personality of recommendation results. Finally， the image content information was matched with the inherent attribute information of image and the user profile model to achieve accurate and intelligent recommendations of remote sensing data. Comparative experiments were conducted on real order data， to compare the proposed method with the newer recommendation method based solely on image attribute information. Experimental results show that the proposed method achieves a 70% improvement in the discrimination of positive and negative samples on the experimental data compared to the recommendation method considering user portrait. When using 10% training data with similar consumption time， the recommendation error rate decreases by 4.0 - 5.6 percentage points compared to the comparison method. When using 100% training data， the recommendation error rate decreases by 0.6 - 1.0 percentage points. These results validate the feasibility and effectiveness of the proposed method.

It is difficult to judge the image multiply distortion type. In order to solve the problem， based on the idea of deep learning multi-label classification， a new multiply distortion type judgement method based on multi-scale and multi-classifier Convolutional Neural Network （CNN） was proposed. Firstly， the image block containing high-frequency information was obtained from the image， and the image block was input into the convolution layers of different receptive fields to extract the shallow feature maps of the image. Then， the shallow feature maps were input into the structure of each sub-classifier for deep feature extraction and fusion， and the fused features were judged by the Sigmoid classifier. Finally， the judgment results of different sub-classifiers were fused to obtain the multiply distortion type of image. Experimental results show that， on the Natural Scene Mixed Disordered Images Database （NSMDID）， the average judgment accuracy of the proposed method can reach 91.4% for different types of multiply distortion types in the images， and most of them are above 96.8%， illustrating that the proposed method can effectively judge the types of distortion in multiply distortion images.

The Quality of Experience (QoE) of audio stream is significantly affected by packet loss in packet-switch network. To control the loss and analyze its effect on QoE, a simulated multimedia transport test bed was designed. And the mapping model between packet loss rate and QoE was established by regression analysis under specific encoding/decoding mode and Real-time Transport Protocol (RTP) packet interval. The model has low computational complexity and can predicate the impairment of packet loss on user experience in real-time.