Aiming at the problems of inaccurate traction load recognition in electric tractor plowing and cultivating operation and the dependence of the training process on massive labeled data, a training framework based on semi-supervised learning algorithm for fusion of multiple working condition parameters in electric tractor plowing and cultivating operation was proposed, and a model for electric tractor traction load grade recognition based on wide residual network and extended long and short-term memory network (WideResNet-xLSTM, WRNx) was constructed. Among them, the semi-supervised learning framework used two kinds of data, labeled and unlabeled, for the iterative training of the discriminative model, and applied C-means fuzzy clustering to analyze the linear output of the model;based on the WRNx combinatorial model, the effective features of the load data were deeply extracted through the feature expression capability of WideResNet, and the temporal relationship was processed through the xLSTM network, and finally, the load sequence was realized by the classifier for classification prediction. A multi-sensor-load parameter testing system for electric tractor plowing and tilling units was constructed and field tests for plowing and tilling operations were carried out. The results indicated that the semi-supervised learning framework proposed can reduce the training sample requirement of labeled data by 25.4%, which was better than the traditional supervised learning training framework, and the accuracy and F1-score of the model constructed for recognizing the hauling class of electric tractor plowing and cultivating operation were 94.35% and 94.27%, respectively. The research result can provide a solution for semi-supervised learning to recognize the load of electric tractor plowing operation.