Fingerprint features and localization model are two key factors affecting the localization accuracy in fingerprint localization methods. In terms of fingerprint feature, the stability of visible light intensity is high, but the discrimination of position features is low. The radio signal strength is highly distinguishable, but has strong volatility. Meanwhile, the localization models based on convolutional neural networks (CNN) can not effectively highlight important features during feature extraction. In view of the above problems, a hybrid fingerprint indoor localization method based on ECA-CNN (ECACon-HF) is proposed in this paper. First, we use visible light intensity and received signal strength indication (RSSI) of Bluetooth low energy (BLE) to construct hybrid fingerprints, reduce the influence of instability of BLE fingerprints, and enhance the discrimination between different positions. Meanwhile, the CNN localization model is improved by the efficient channel attention (ECA). ECA can adaptively extract important information in fingerprints through cross-channel interaction strategies, suppress environmental interference in fingerprints, enhance the expression ability of hybrid fingerprint features, and make more effective use of the advantages of hybrid fingerprints. The experimental results show that the ECACon-HF method proposed in this paper achieves the localization precision of 0.316m, higher accuracy than on the single fingerprint. Meanwhile, based on the same fingerprint database, the proposed method outperforms other related indoor localization methods.