The Lattice series model using potential words information has been proved to be effective in Chinese Named Entity Recognition (NER). The recently proposed Simplified Lattice not only brings new baseline results, but also improves the inference speed of Lattice models. However, the Simplified Lattice fails to fully explore the rich information contained in the radical-level features of the character sequences. Moreover, the performance of the Simplified Lattice decreases dramatically as the length of entity increases. In this paper, we propose the SLRL-NER model that integrates word, character, and radical-level information to alleviate the above problems. Specifically, text Convolutional Neural Network (CNN) is used to extract the radical-level features. The original SoftLexicon set is expanded to refine the relative position information of characters in the candidate words to cope with the challenge of increasing entity length. Experiments on three datasets show SLRL-NER outperforms the state-of-the-art comparison methods.