In real life, many data samples of systems can be easily obtained, but only a small part of accurate laabels can be obtained. In order to obtain a better classification learning model, a semi-supervised classification algorithm based on weight diversity was proposed by introducing semi-supervised learning and improving Unlabeled Data to Enhance Ensemble Diversity (UDEED), namely UDEED ⁺. In UDEED ⁺, based on the prediction disagreement of unlabeled data by base learners, the loss of weight diversity was proposed. The disagreement between base learners was represented by the cosine similarity of the weights of base learners. The diversity of model was fully expanded from different perspectives of loss function, and the unlabeled data were used to encourage the diversity representation of ensemble learners in the process of model training, so as to improve the performance and generalization of the classification learning model. Comparative experiments were conducted on 8 UCI public datasets with semi-supervised algorithms of UDEED algorithm, Safe Semi-Supervised Support Vector Machine (S4VM) and Semi-Supervised Weak-Label (SSWL). Compared with UDEED, UDEED ⁺ has the accuracy and F1 score improved by 1.4 percentage points and 1.1 percentage points respectively; compared with S4VM, UDEED ⁺ has the accuracy and F1 score improved by 1.3 percentage points and 3.1 percentage points respectively; compared with UDEED, UDEED ⁺ has the accuracy and F1 score improved by 0.7 percentage points and 1.5 percentage points respectively. Experimental results illustrate that the increase of weight diversity can improve the classification performance of the model, verifying its positive effect on the improvement of the classification performance of UDEED ⁺.