China has entered a “post-poverty alleviation” era, where the achievement of sustainable livelihoods by farmers has become a focus. This study used the China Family Panel Studies (CFPS) database, which was constructed based on an analysis of the DFID sustainable livelihood framework, and built a sustainable livelihood index system for farmers using the entropy weight method to measure the weights of sustainable livelihood indexes and calculate a sustainable livelihood index. This study used the Tobit model to discuss the impacts of different types of risk on the achievement of a sustainable livelihood by farmers. The results showed that environmental risk, chronic disease risk, and major disease risk all had significant negative impacts on the ability of farmers to achieve a sustainable livelihood. The impacts of major disease and chronic disease risks on the achievement of a sustainable livelihood by farmers living in plain areas were stronger than those associated with environmental risk. In China, the environmental risks were complex and diverse and were the most important factors that affect the achievement of a sustainable livelihood by rural households in mountainous areas. Chronic disease risk was also an important adverse factor that affected the achievement of a sustainable livelihood by rural households in mountainous areas.

With the continuous development of the internet and big data, modernization and informatization are rapidly being realized in the agricultural field. In this line, the volume of agricultural news is also increasing. This explosion of agricultural news has made accurate access to agricultural news difficult, and the spread of news about some agricultural technologies has slowed down, resulting in certain hindrance to the development of agriculture. To address this problem, we apply NLP to agricultural news texts to classify the agricultural news, in order to ultimately improve the efficiency of agricultural news dissemination. We propose a classification model based on ERNIE + DPCNN, ERNIE, EGC, and Word2Vec + TextCNN as sub-models for Chinese short-agriculture text classification (E3W), utilizing the GreedySoup weighting strategy and multi-model combination; specifically, E3W consists of four sub-models, the output of which is processed using the GreedySoup weighting strategy. In the E3W model, we divide the classification process into two steps: in the first step, the text is passed through the four independent sub-models to obtain an initial classification result given by each sub-model; in the second step, the model considers the relationship between the initial classification result and the sub-models, and assigns weights to this initial classification result. The final category with the highest weight is used as the output of E3W. To fully evaluate the effectiveness of the E3W model, the accuracy, precision, recall, and F1-score are used as evaluation metrics in this paper. We conduct multiple sets of comparative experiments on a self-constructed agricultural data set, comparing E3W and its sub-models, as well as performing ablation experiments. The results demonstrate that the E3W model can improve the average accuracy by 1.02%, the average precision by 1.62%, the average recall by 1.21%, and the average F1-score by 1.02%. Overall, E3W can achieve state-of-the-art performance in Chinese agricultural news classification.

In view of the diversity and complexity of the existing travel time prediction methods, travel time is an important indicator to quantify the state of road traffic congestion. In this paper, based on the analysis of various travel time prediction methods, a network model based on the idea of BP algorithm is constructed by SPSS software, and the method of travel time prediction of urban road is studied by combining BP neural network with three indexes affecting vehicle speed. Firstly, taking Jinshui road of Zhengzhou as the research object, this paper obtains the data of three factors that affect the vehicle speed, namely, vehicle spacing, flow and density; then, it constructs the neural network model and analyzes the data with the help of SPSS; finally, the experimental results are obtained, and the analysis shows that the error between the predicted speed and the real speed is very small, the average predicted error value is 0.3, and the average error rate is 2.35%, the result has better accuracy and feasibility, which can further realize the effective prediction of the travel time of the car. The model established in this paper has better practicability for travel time prediction, and the data can be used as reference value for travel time prediction of intelligent transportation system and other systems.

Accurate forecasting of agricultural product prices is vital for stabilizing market dynamics and informing effective policy decisions. However, the strong nonlinearity and complexity of influencing factors often limit the performance of traditional models. To address these challenges, this study proposes a hybrid forecasting framework—GRU-IHEOA-N-BEATS-BP—targeted at apple and crucian carp price prediction. The model first utilizes STL decomposition to separate the time series into seasonal, trend, and residual components. Pearson correlation analysis is applied to identify key influencing factors for each component. A GRU network is then constructed to model sequential dependencies, with its parameters optimized by an improved Human Evolutionary Optimization Algorithm (IHEOA) to enhance prediction accuracy. Concurrently, an N-BEATS model captures high-order nonlinear patterns from the decomposed components. Finally, a BP neural network aggregates outputs from all forecasting submodels to generate the final prediction. Experimental results validate the effectiveness of the proposed model. For crucian carp, the value R ² improves from 64. 67% (with N-BEATS standalone) to 97. 95\%, while MAE and MSE decrease by 81.5% and 93.6%, respectively. These results demonstrate the model’s superior ability to capture complex temporal and nonlinear characteristics. In summary, this hybrid approach significantly enhances the accuracy and robustness of forecasting. It provides a valuable tool for intelligent agricultural management, contributing to food security, higher farmer income, and more precise market regulation.