With the widespread adoption of large model technologies, the quality of intelligent adversarial information weapons has significantly improved. The “malicious cognitive behavior strategies” embedded within large models may directly impact the ethical norms and security safeguards of information warfare in the intelligent era. To uncover model biases and reveal differences among word vector libraries across diverse cultural contexts, a quantitative analysis of the ideological orientation of word embeddings from static large models was conducted. Five representative word vector libraries were selected, and a quantitative evaluation paradigm based on positive distance, negative distance, and tendency scores was proposed, using carefully curated test words. By computing the Euclidean distances between test words and benchmark terms and applying a normalization method to derive tendency scores, an intuitive quantitative metric was established, enabling precise quantification of ideological tendencies in word vectors. Analysis of both pre-training corpora and resulting embeddings directly exposes the endogenous biases inherent in the models. Experimental results show that word vector libraries trained on different corpora exhibit significant differences in ideological orientation, and the proposed “Euclidean distance-tendency score” evaluation paradigm can objectively differentiate among these libraries. The study concludes that the proposed method provides an operational framework for bias detection and “value alignment” in large models, applicable to word vector analysis across languages and cultural backgrounds, thereby facilitating the identification of potential value deviations prior to model deployment.