Although the popular database systems perform well on query optimization, they still face poor query execution plans when the join operations across multiple tables are complex. Bad execution planning usually results in bad cardinality estimations. The cardinality estimation models in traditional databases cannot provide high-quality estimation, because they are not capable of capturing the correlation between multiple tables in an effective fashion. Recently, the state-of-the-art learning-based cardinality estimation is estimated to work better than the traditional empirical methods. Basically, they used deep neural networks to compute the relationships and correlations of tables. In this paper, we propose a vertical scanning convolutional neural network (abbreviated as VSCNN) to capture the relationships between words in the word vector in order to generate a feature map. The proposed learning-based cardinality estimator converts Structured Query Language (SQL) queries from a sentence to a word vector and we encode table names in the one-hot encoding method and the samples into bitmaps, separately, and then merge them to obtain enough semantic information from data samples. In particular, the feature map obtained by VSCNN contains semantic information including tables, joins, and predicates about SQL queries. Importantly, in order to improve the accuracy of cardinality estimation, we propose the negative sampling method for training the word vector by gradient descent from the base table and compress it into a bitmap. Extensive experiments are conducted and the results show that the estimation quality of q-error of the proposed vertical scanning convolutional neural network based model is reduced by at least 14.6％ when compared with the estimators in traditional databases.