Aiming at the problem of exponential growth of data dimension faced by Quantum State Tomography （QST）， a Broad QST model based on Adaptive Feature Extraction （AFE_BQST） was proposed. Firstly， an adaptive feature extraction strategy was introduced to avoid the uncertainty of mapping feature nodes caused by random generation of weights. Secondly， Broad Learning System （BLS） was used to map the input data to a more appropriate feature space in a non-iterative way for feature extraction of large-capacity data. Finally， experiments were executed in the cases of low- and high-dimensional quantum state data to compare AFE_BQST with Broad QST （BQST）， Deep neural network QST （D_QST）， Convolutional neural network QST （C_QST） and U-shaped network QST （U_QST） models by using two indicators of average fidelity and running time. Experimental results show that in the case of small samples with low-dimensional quantum state， compared with the sub-optimal baseline model BQST， AFE_BQST improves the fidelity by 0.045 percentage points with the similar running time； in the case of large samples with high-dimensional quantum state， compared with the sub-optimal baseline model D_QST， AFE_BQST improves the fidelity by 0.175 percentage points with the running time reduced by 99%. The above results prove that AFE_BQST is able to extract quantum state data features adaptively and reconstruct quantum state data accurately and efficiently.