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With the development of large language models (LLMs), there has been an increasing need for significant advancements in handling long contexts. To enhance long-context capabilities, constructing high-quality training data with **long-range dependencies** is crucial. Existing methods to select long-context data often rely on sentence-level analysis,which can be greatly optimized in both performance and efficiency. In this paper, we propose a novel token-level framework, **LongAttn** , which leverages the self-attention mechanism of LLMs to measure the **long-range dependencies** for the data. By calculating token-level dependency strength and distribution uniformity of token scores, LongAttn effectively quantifies **long-range dependencies** , enabling more accurate and efficient data selection. We filter **LongABC-32K** from open-source long-context datasets (ArXiv, Book, and Code). Through our comprehensive experiments, LongAttn has demonstrated its excellent **effectiveness** , **scalability** , and **efficiency** . We will release our code and the high-quality long-context dataset **LongABC-32K** in the future.