For a long time, video copyright infringement problems have emerged one after another, and the detection of video similarity is an important approach of identifying video copyright infringement. Concerning the problems of the correlation difficulty of multi-feature relation and high time complexity in the existing video similarity detection methods, a fast comparison method based on perceptual hashing and dicing was proposed. First, the key image frames of the video were used to generate a digital fingerprint set. Then, based on the dicing method, the corresponding inverted index was generated to speed up the comparison between digital fingerprints. Finally, the similarity was judged according to the obtained Hamming distance between the digital fingerprints. Experimental results show that the proposed method can reduce the detection time by an average of 93% with ensuring the detection accuracy compared to the traditional perceptual hashing comparison methods; in the comparison with three common methods including Multi-Feature Hashing (MFH), Self-Taught Hashing (STH) and SPectral Hashing (SPH), the mean Average Precision (mAP) of the proposed method is increased by 1.4%, 2% and 2.3%,respectively, and the detection time is shortened by 25%, 32% and 16%, respectively, which verifies the feasibility of the proposed method.

Concerning the problems in traditional house price evaluation method, such as single data source, over-reliance on subjective experience, idealization of considerations, an intelligent evaluation method based on multi-source data and ensemble learning was proposed. First, feature set was constructed from multi-source data, and the optimal feature subset was extracted using Pearson correlation coefficient and sequential forward selection method. Then, with Bagging ensemble strategy used as a combination method, multiple Light Gradient Boosting Machines (LightGBMs) were integrated based on the constructed features, and the model was optimized by using Bayesian optimization algorithm. Finally, this method was applied to the problem of house price evaluation, and the intelligent evaluation of house prices was realized. Experimental results on the real house price dataset show that, compared with traditional models such as Support Vector Machine (SVM) and random forest, the new model introduced with ensemble learning and Bayesian optimization improves the evaluation accuracy by 3.15%, and the evaluation results with percent error within 10% account for 84.09%. It can be seen that, the proposed model can be well applied to the field of intelligent house price evaluation, and has more accurate evaluation results.