Understanding the meaning of mathematical problems is the key for automatic problem solving. However， the accuracy of understanding word problems with complex situations and many parameters is relatively low in previous studies， and the effective optimization solutions need to be further explored and studied. On this basis， a math word problem understanding method integrating commonsense knowledge base and grammatical features was proposed for the classical probability word problems with complex context. Firstly， a classical probability word problem representation model containing seven kinds of key problem-solving parameters was constructed according to text and structure characteristics of the classical probability word problems. Then， based on this model， the task of understanding of word problems was transformed into the problem of solving parameter identification， and a Conditional Random Field （CRF） parameter identification method integrating multi-dimensional grammatical features was presented to solve it. Furthermore， aiming at the problem of implicit parameter identification， a commonsense completion module was added， and an understanding method of math word problems integrating commonsense knowledge base and grammatical features was proposed. Experimental results show that the proposed method has the average F1-score of 93.56% for problem-solving parameter identification， and the accuracy of word problem understanding reached 66.54%， which are better than those of Maximum Entropy Model （MaxEnt）， Bidirectional Long Short-Term Memory-Conditional Random Field （BiLSTM-CRF） and traditional CRF methods. It proves the effectiveness of this method in understanding of classical probability word problems.

With the continuous development of network applications， network resources are growing exponentially and information overload is becoming increasingly serious， so how to efficiently obtain the resources that meet the user needs has become one of the problems that bothering people. Recommendation system can effectively filter mass information and recommend the resources that meet the users needs. The research status of the recommendation system was introduced in detail， including three traditional recommendation methods of content-based recommendation， collaborative filtering recommendation and hybrid recommendation， and the research progress of four common deep learning recommendation models based on Convolutional Neural Network （CNN）， Deep Neural Network （DNN）， Recurrent Neural Network （RNN） and Graph Neural Network （GNN） were analyzed in focus. The commonly used datasets in recommendation field were summarized， and the differences between the traditional recommendation algorithms and the deep learning-based recommendation algorithms were analyzed and compared. Finally， the representative recommendation models in practical applications were summarized， and the challenges and the future research directions of recommendation system were discussed.

Body size parameters are important indicators to evaluate the growth status of sheep. How to achieve the measurement with non-stress instrument is an urgent and important problem that needs to be resolved in the breeding process of sheep. This paper introduced corresponding machine vision methods to measure the parameters. Sheep body in complex environment was detected by gray-based background subtraction method and chromaticity invariance principle. By virtue of grid method, the contour envelope of sheep body was extracted. After analyzing the contour sequence with D-P algorithm and Helen-Qin Jiushao formula, the point with maximum curvature in the contour was acquired. The point was chosen as the measurement point at the hip of sheep. Based on the above information, the other three measurment points were attained using four-point method and combing the spatial resolution, the body size parameters of sheep body were acquired. And the contactless measurement was achieved. The experimental results show that, the proposed method can effectively extract sheep body in complex environment; the measurement point at hip of sheep can be stably determined and the height of sheep can be stably attained. Due to the complexity of the ambient light, there still exits some problems when determining the shoulder points.

Although the normal cloud model is universally used, it faces some difficulties when describing some monotonic rise/fall conceptions. This model also has big subjective influence under multiple conditions and large computation consumption. To overcome these shortcomings, a new kind of exponential cloud model was provided along with a weight based cloud reasoning algorithm. By splitting the multi-condition generator to several single-condition generators, the algorithm firstly used Analytic Hierarchy Process (AHP) method to get weight of each property, and then used them to calculate weighted average of single-condition generator output to quantitfy value. The validation and effectiveness of this method is checked through a comparison between fuzzy reasoning and stimulation of torpedo avoid system.

It is indicated that Cao Guoliang et al's multicast key management scheme based on generalized cat map does not satisfy the independent security requirement of individual keys of group members. A modification scheme was proposed by using one way function. The presented scheme satisfies the independent security requirement of individual keys of group members. Compared with Cao Guoliang et al's scheme, this modification scheme has higher security and lower computation and communication overheads.