Recent advancements in recommendation algorithms have increasingly adopted graph contrastive learning (GCL) to optimize collaborative filtering (CF) frameworks. Extensive research has shown its effectiveness in mitigating data sparsity challenges. The current common method is to construct a comparison view using random perturbation, and then align the corresponding nodes. We believe that this approach has some issues. Constructing additional views can result in a lack of some real interactions or false interactions in the comparison view. In addition, current research focuses on aligning nodes of the same type (between users or projects), neglecting the interaction modeling between users and projects. Our work proposes LTL-GCL, a GCL-enhanced recommendation algorithm designed to overcome these limitations. The original embeddings are fed into a two-layer GCN, with the resulting output serving as a synthetic contrastive view. The generated view does not lose any real interaction information and does not require GCN execution on the pseudo view. In addition, we have added a node alignment loss between users and items during the training phase. To evaluate our approach, we performed comprehensive testing across three real-world datasets, assessing both computational performance and recommendation quality.