Based on big data, this paper studies the influence of new type of filling pneumonia on the development of sports industry. When selecting the typical economic indicators that reflect the development trend of sports industry, it is found that the data is huge according to the big industrial data, but the information that can be reflected is poor and complex. Therefore, it is necessary to process these big economic data in order to obtain the impact of new coronary pneumonia on the development of sports industry. This paper studies the feature selection algorithm of big data samples, so as to select typical economic indicators from many economic indicators of sports industry to reflect the development trend of sports industry. A deep learning algorithm based on feature selection of big data is proposed. Firstly, a feature selection framework for big data is constructed, and then data fusion and deep learning are carried out. Experiments show that the algorithm can solve the contradiction between large data and poor information. This method has a certain forward-looking, and has a certain reference value for the information discrimination of the development trend of sports industry.

This paper presents an in-depth study and analysis of the correlation between satisfaction with rural residents’ income and mental health well-being in the context of industrial structure upgrading. Most of the studies on residents’ subjective well-being from the perspective of relative income or income inequality have started from the happiness of rural residents and the satisfaction of rural residents’ life, and few scholars have focused on the psychological health of rural residents. Subjective well-being is significantly related to external and internal goals in desire. Life satisfaction is significantly and positively correlated with external and internal goals, as well as the six dimensions of desire, except for social identity; positive emotions are significantly and positively correlated with internal goals; negative emotions are only negatively correlated with self-acceptance, and there is a significant positive correlation between income level and desire. In vertical income, there is also a process of judging whether the expected income is achieved. If the expected income growth level is achieved, the income satisfaction will increase. Desire mediates the effect of income level on subjective well-being. Income level influences subjective well-being through internal goals; income level influences life satisfaction and positive emotions through external goals. The relationship between income inequality and mental health is influenced by the characteristics of the population, with women and middle-aged people being the most negatively affected. This relationship is also influenced by income level, with the effect of income inequality on mental health showing a negative effect in the lower and middle-income groups but a positive effect in the higher income groups. Income inequality affects residents’ mental health through the mediating effects of a sense of social justice, life stress, and trust in government. Inequality in household wealth can exacerbate the negative effects of income inequality on mental health.

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture.

Industrial integration provides an important tool to promote the revitalization of rural industries and is an effective way to promote sustainable income growth for farmers and to build a well-off society. The main implementer of rural industrial integration are individual farmers. The fundamental state of life of the farm households determines the implement ability of industrial farming cooperation. The development of rural industries has played an irreplaceable role in the process of poverty eradication in China. Promoting the integration of the three rural industries can play an important role in consolidating the fight against poverty and bridging the stage of rural revitalization to continuously play a role in the development of rural industries and lay the foundation for the smooth implementation of rural revitalization. Whereas human capital was one of the important perspectives in studying the integration of three rural industries, the relationship between human capital in education and the integration of three rural industries was empirically analyzed based on data from the China Household Tracking Survey (CFPS). With the help of a Random Forest model algorithm, the importance of key elements affecting the integration of rural industries was measured and ranked.