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The strategy of rural revitalization is to take measures to deal with the big problem of rural decline, mainly from the aspects of developing rural economy, getting rid of poverty, and adjusting rural structure, and changing the traditional thinking of rural development when implementing policies. With the number and scale of rural laborer’s back home to start undertaking expanding continuously, it is more important to improve the quality of rural laborer’s back home to start undertaking. On the basis of analyzing the motivation and problems of rural laborer’s back home to start undertaking, this paper puts forward a risk identification model based on artificial intelligence (AI) algorithm and explores the development path of rural laborer’s back home to start undertaking under the background of rural revitalization. Through simulation experiments, the effectiveness and superiority of this algorithm are analyzed. The results show that the accuracy rate of venture risk assessment in this paper is 93.95%, and the error is 10.24% lower than that of the back propagation neural network (BPNN). It can be seen that this method has a significant effect in the analysis of the risk and influencing factors of rural laborer’s back home to start undertaking. The overall requirements of rural revitalization strategy for industrial prosperity will inevitably encourage and attract more rural laborer’s to back home to start undertaking, and “starting a business to help the poor” will become a new path for rural poverty alleviation and development in the future.

Background:  Overuse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of an artificial intelligence (AI)-based, automatic, and confidential antibiotic feedback intervention was evaluated to determine whether it could reduce antibiotic prescribing rates and avoid inappropriate prescribing behaviors by physicians. Methods:  A randomized, cross-over, cluster-controlled trial was conducted in 77 primary care institutions of Guizhou Province, China. All institutions were randomly divided into two groups and given either a 3-month intervention followed by a 3-month period without any intervention or vice versa. The intervention consisted of 3 feedback measures: a real-time warning pop-up message of inappropriate antibiotic prescriptions on the prescribing physician’s computer screen, a 10-day antibiotic prescription feedback, and distribution of educational brochures. The primary and secondary outcomes are the 10-day antibiotic prescription rate and 10-day inappropriate antibiotic prescription rate. Results:  There were 37 primary care institutions with 160 physicians in group 1 (intervention followed by control) and 40 primary care institutions with 168 physicians in group 2 (control followed by intervention). There were no significant differences in antibiotic prescription rates (32.1% vs 35.6%) and inappropriate antibiotic prescription rates (69.1% vs 72.0%) between the two groups at baseline ( p  = 0.085, p  = 0.072). After 3 months (cross-over point), antibiotic prescription rates and inappropriate antibiotic prescription rates decreased significantly faster in group 1 (11.9% vs 12.3%, p  < 0.001) compared to group 2 (4.5% vs 3.1%, p  < 0.001). At the end point, the decreases in antibiotic prescription rates were significantly lower in group 1 compared to group 2 (2.6% vs 11.7%, p  < 0.001). During the same period, the inappropriate antibiotic prescription rates decreased in group 2 (15.9%, p  < 0.001) while the rates increased in group 1 (7.3%, p  < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates.  Conclusion:  The conclusion is that artificial intelligence based real-time pop-up of prescription inappropriate warning, the 10-day prescription information feedback intervention, and the distribution of educational brochures can effectively reduce the rate of antibiotic prescription and inappropriate rate.