Populus euphratica, Tamarix ramosissima, and Sophora alopecuroides are, respectively, typical arboreal, shrubby, and herbaceous species in oases of arid regions. It is important to study the difference in metabolic characteristics of the rhizosphere fungal community of these plant species and their relationships with soil factors for the preservation of delicate arid oasis ecosystems with future environmental changes. In this study, we, respectively, collected 18 rhizosphere soil samples of P. euphratica, T. ramosissima, and S. alopecuroides to explore the difference in rhizosphere fungal metabolic characteristics of different plant life forms and their underlying driving factors. The results showed that (1) soil physicochemical properties (including soil water content, pH, etc.) were significantly different among different plant species (p < 0.05). (2) Rhizosphere fungal metabolic characteristics were significantly different between S. alopecuroides and T. ramosissima (ANOSIM, p < 0.05), which was mainly caused by the different utilization of carboxylic carbon. (3) The RDA showed that the main driving factors of the variations in rhizosphere fungal metabolic characteristics were different among different plant species. The main explanatory variables of the variations in the metabolic characteristics of the rhizosphere fungal community were carbon to nitrogen ratio (23%) and available potassium (17.4%) for P. euphratica, while soil organic carbon (23.1%), pH (8.6%), and total nitrogen (8.2%) for T. ramosissima, and soil clay content (36.6%) and soil organic carbon (12.6%) for S. alopecuroides. In conclusion, the variations in rhizosphere fungal metabolic characteristics in arid oases are dominantly affected by soil factors rather than plant life forms.

Vegetable products have the characteristics of short shelf life. If they can't be sold out in time, it will cause waste, which will lead to the loss of merchants. Therefore, it is of great significance to formulate reasonable purchase quantity and pricing strategy. In view of this, this paper studies the purchase quantity and pricing strategy of vegetable commodities by establishing multiple linear regression model, ARIMA time series model and exponential smoothing time model. The results show that: (1) the relationship between consumers' purchase of various vegetables can be understood through correlation analysis; (2) By establishing time series, exponential smoothing model and combining multiple linear regression, the daily sales volume and daily replenishment volume can be better predicted, thus guiding pricing decision. (3) In the process of vegetable pricing, combining the historical pricing method can make the pricing decision more reliable, and adopting different pricing strategies for different vegetables according to consumers' purchasing psychology is conducive to increasing vegetable sales.