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Current methods rely on pre-trained models for rich semantic text embeddings, but the challenge of anisotropy may affect subsequent encoding quality. Additionally, existing models may struggle with noise detection. To address these issues, we propose JCLB, a novel model that <jats:underline>J<\/jats:underline>oins <jats:underline>C<\/jats:underline>ontrastive <jats:underline>L<\/jats:underline>earning and <jats:underline>B<\/jats:underline>elief rule base for NER in cybersecurity. JCLB utilizes contrastive learning to enhance similarity in the vector space between token sequence representations of entities in the same category. A Belief Rule Base (BRB) is developed using regexes to ensure accurate entity identification, particularly for fixed-format phrases lacking semantics. Moreover, a Distributed Constraint Covariance Matrix Adaptation Evolution Strategy (D-CMA-ES) algorithm is introduced for BRB parameter optimization. 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