Long text matching is a basic work of natural language processing, and it plays a key role in text clustering, news recommendation, etc. Due to the limitations of the corpus, space structure, and text representation technology, long text matching has been progressing slowly. The bidirectional encoder representations from Transformer (BERT) model proposed in recent years has an excellent performance in the text representation. For BERT, there are three common methods for processing long texts: truncation, segmentation, and compression. The truncation method causes the loss of massive text information; the segmentation method retains text information but loses part of the semantic information; the compression method may lose part of the key information. In response to the above problems, this study improves the segmentation method and proposes a long text matching model based on BERT (LTM-B), which is based on the Siamese neural network and adopts a layered idea to divide the document into multiple segments. The BERT model is used for text vectorization. As a result, the matrix representation of the document is obtained. The bidirectional long short-term memory (BiLSTM) is employed to generate the position matrix, and then the sum of the document matrix and the position matrix is input to the Transformer encoder for feature extraction. Finally, the two matrices are interacted, pooled, and spliced, and then the matching results are output through the fully connected layer classification. Experiments show that the LTM-B model outperforms other methods in long text matching.