Residential commodity price is not only an important index of government macro-control, but also an important livelihood topic in society. The purpose of this paper is to take Sanya as an example to make an empirical analysis on the relationship between the factors affecting commodity housing prices, and their effects on housing prices. By using pearson correlation analysis, factor analysis and principal component regression method, it is found that the main factors influencing the housing price in Sanya are the housing sales area, the gross domestic product, the per capita disposable income, and the land price level of residential land. Among them, housing sales area and Sanya city house price changes in the opposite direction, the regional gross domestic product, per capita disposable income, residential land price level plays a positive role in housing prices.

“Take photos to make money” APP is a self-service crowdsourcing platform based on mobile internet, providing enterprises with a variety of business inspection and information collection. Whether the task’s pricing is reasonable is the key to whether APP can play its role effectively. Based on the data of “China Undergraduate Mathematical Contest in Modeling” in 2017 and taking Guangzhou as an example, this paper studies the factors affecting the task pricing and the reasons for the unfinished tasks by using the multiple linear regression model and K-Means clustering analysis, and obtains a new price scheme combining BP neural network and ergodic search algorithm. Compared with the original scheme, not only the completion rate of the new pricing scheme is improved, but also the cost is reduced, which better meets the interests of both members and entrepreneurs.