Recommender systems increasingly leverage graph-based representations to model complex user-item relationships. To alleviate interaction sparsity, graph contrastive learning (GCL) incorporates self-supervised signals to improve recommendation accuracy. However, existing GCL-based methods suffer from two key limitations: (i) graph perturbation-based contrastive views may distort structural information and degrade embedding quality; (ii) optimization via random negative sampling reduces training efficiency and recommendation quality. To address these challenges, we introduce a novel framework named Graph Contrastive Learning with Convolutional Perturbation (GCLCP) for recommendation. GCLCP introduces perturbations to the neighborhood aggregation in graph convolution, generating contrastive views while preserving the graph structure. Furthermore, inspired by the concepts of alignment and uniformity in representation learning, we incorporate these objectives into the GCL framework as loss functions, thereby improving both efficiency and accuracy without relying on negative sampling. Experiments conducted on three benchmark datasets demonstrate the superior performance of GCLCP compared to the representative baselines. Specifically, on the iFashion dataset, it achieves a 6.04% improvement in recommendation accuracy, measured by NDCG@20.