Many fields of scientific simulation, such as chemistry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we center around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address these difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained partitioning strategy based on element-wise tensor contraction. By analyzing and selecting appropriate dimension partitioning strategies, we can efficiently utilize the multi-threading parallelism on GPUs and optimize the overall performance of GSpTC. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merging phase, which greatly accelerates the computation phase in sparse tensor contraction computations. Furthermore, GSpTC employs parallel pipeline technology to hide the data transmission time between the host and the device, further enhancing its performance. As a result, GSpTC achieves an average performance improvement of 267% compared to the previous state-of-the-art framework Sparta.