Quality issues, such as errors or deficiencies in triplets, become increasingly prominent in knowledge graphs, severely affecting the credibility of downstream applications. Accuracy evaluation is crucial for building confidence in the use and optimization of knowledge graphs. An embedding-model-based method is proposed to reduce reliance on manually labeled data and to achieve scalable automatic evaluation. Triplet verification is formulated as an automated threshold selection problem, with three threshold selection strategies proposed to enhance the robustness of the evaluation. In addition, triplet importance indicators are incorporated to place greater emphasis on critical triplets, with importance scores defined based on network structure and relationship semantics. Experiments are conducted to analyze and compare the impact on performance from various perspectives, such as embedding model capacity, knowledge graph sparsity, and triplet importance definition. The results demonstrate that, compared to existing automated evaluation methods, the proposed method can significantly reduce evaluation errors by nearly 30% in zero-shot conditions, particularly on datasets of dense graphs with high error rates.