In indoor environments, Global Positioning Systems (GPS) often fail to provide accurate positioning services, leading to an increased demand for indoor positioning technology. Indoor positioning techniques commonly utilize Received Signal Strength (RSS) and Angle of Arrival (AOA) as key information. However, the variability in RSS data and the impact of indoor multipath effects on AOA estimation can lead to a lack of stability and accuracy in positioning. Consequently, this paper proposes a novel indoor positioning algorithm based on Time Sequences Fusion in Pauta criterion-Correlation coefficient Convolutional Neural Networks (P-C-CNN). The P-C-CNN approach integrates data points from different nodes and various time sequences, leveraging the interconnectedness of temporal and spatial data to enhance the accuracy and reliability of indoor positioning. Firstly, this method utilizes the P-C algorithm to remove outliers in AOA-RSS data, improving the quality of the training data. Secondly, the algorithm randomly selects data at intervals, reducing the training time of the model and effectively simulating the uncertainty of data selection in the online positioning phase, thus reducing overfitting of the model to the training data. Furthermore, the traditional single-frame information training method is unable to stably extract information features due to the mixture of noise. The proposed algorithm randomly selects multiple frames of fixed length from the continuously collected AOA-RSS data within time sequences fusion, and then employs CNN for feature extraction. This approach can avoid the issue of large error fluctuations commonly encountered in single-frame signal positioning. Finally, through extensive practical testing, this paper has validated the effectiveness of the proposed method. The experimental results demonstrate that in typical indoor environments, compared to fingerprint positioning algorithms that solely rely on RSS data or AOA information, the proposed algorithm achieves an improved classification accuracy from 91.6% to 96.4%, the positioning accuracy is improved from 1.3 meters to 0.3 meters. Moreover, compared to the traditional model-based AOA-RSS joint positioning, this algorithm effectively addresses the influence of interference factors such as multipath effects observed in real-world measurements. The positioning accuracy is improved from 1.1 meters to 0.3 meters.