Self-training, a common strategy for tackling the annotated-data scarcity, typically involves acquiring auto-annotated data with high confidence generated by a teacher model as reliable data. However, in low-resource scenarios for Relation Extraction (RE) tasks, this approach is hindered by the limited generalization capacity of the teacher model and the confusable relational categories in tasks. Consequently, efficiently identifying reliable data from automatically labeled data becomes challenging, and a large amount of low-confidence noise data will be generalized. Therefore, this study proposes a self-training approach for low-resource relation extraction (ST-LRE). This approach aids the teacher model in selecting reliable data based on prediction ways of paraphrases, and extracts ambiguous data with reliability from low-confidence data based on partially-labeled modes. Considering the candidate categories of ambiguous data, this study proposes a negative training approach based on the set of negative labels. Finally, a unified approach capable of both positive and negative training is proposed for the integrated training of reliable data and ambiguous data. In the experiments, ST-LRE consistently demonstrates significant improvements in low-resource scenarios of two widely used RE datasets SemEval2010 Task-8 and Re-TACRED.