  The study of human values is essential in both practical and theoretical
domains. With the development of computational linguistics, the creation of
large-scale datasets has made it possible to automatically recognize human
values accurately. SemEval 2023 Task 4\cite{kiesel:2023} provides a set of
arguments and 20 types of human values that are implicitly expressed in each
argument. In this paper, we present our team's solution. We use the
Roberta\cite{liu_roberta_2019} model to obtain the word vector encoding of the
document and propose a multi-head attention mechanism to establish connections
between specific labels and semantic components. Furthermore, we use a
contrastive learning-enhanced K-nearest neighbor
mechanism\cite{su_contrastive_2022} to leverage existing instance information
for prediction. Our approach achieved an F1 score of 0.533 on the test set and
ranked fourth on the leaderboard.
